Weather severity and characterization system

ABSTRACT

A method of presenting weather phenomenon information including receiving weather data. At least one weather phenomenon represented by the weather data is identified. A plurality of current parameters related to the current state of the at least one weather phenomenon is determined. A plurality of historical parameters corresponding to one or more previous states of the at least one weather phenomenon is associated with the current state of the at least one weather phenomenon if at least one previous state of the at least one weather phenomenon has been identified. A plurality of forecasted parameters for the at least one weather phenomenon is calculated. Characteristics of the at least one weather phenomenon based on at least a first subset of the current parameters, the historical parameters, and the forecasted parameters are displayed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/789,148, entitled Weather Severity and Characterization System, filedApr. 24, 2007, now U.S. Pat. No. 7,558,674, which claims the benefit ofU.S. Provisional Patent Application No. 60/794,373, entitled WeatherSeverity and Characterization System, filed Apr. 24, 2006, the entiredisclosures of which are incorporated herein by reference.

This application is related to co-pending to U.S. patent applicationSer. No. 11/404,627, entitled, Intelligent Broadcast Presentation Systemand Method, filed Apr. 14, 2006; U.S. patent application Ser. No.11/975,566, entitled Weather Severity and Characterization System, filedOct. 19, 2007; and co-pending U.S. patent application Ser. No.11/975,565, entitled Weather Severity and Characterization System, filedOct. 19, 2007; and U.S. patent application Ser. No. 11/975,747, entitledWeather Severity and Characterization System, filed Oct. 19, 2007, theentire disclosures of which are incorporated herein by reference.

COPYRIGHT NOTICE AND AUTHORIZATION

Portions of the documentation in this patent document contain materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice file or records, but otherwise reserves all copyright rightswhatsoever.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofpreferred embodiments of the invention, will be better understood whenread in conjunction with the appended drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentswhich are presently preferred. It should be understood, however, thatthe invention is not limited to the precise arrangements andinstrumentalities shown.

In the Drawings:

FIG. 1 is a use case diagram for an intelligent broadcast system inaccordance with one embodiment of the present invention;

FIG. 2 is an exemplary graphical presentation in accordance with theintelligent broadcast system of FIG. 1;

FIG. 3 is an exemplary graphical presentation in accordance with theintelligent broadcast system of FIG. 1;

FIG. 4 is an exemplary graphical presentation in accordance with theintelligent broadcast system of FIG. 1;

FIG. 5 is an exemplary graphical presentation in accordance with theintelligent broadcast system of FIG. 1;

FIG. 6 is an exemplary graphical presentation in accordance with theintelligent broadcast system of FIG. 1;

FIG. 7 is an exemplary graphical presentation in accordance with theintelligent broadcast system of FIG. 1;

FIG. 8 is an exemplary graphical presentation in accordance with theintelligent broadcast system of FIG. 1;

FIG. 9A shows a weather event hierarchy in accordance with oneembodiment of the intelligent broadcast system;

FIG. 9B shows a traffic event hierarchy in accordance with oneembodiment of the intelligent broadcast system;

FIGS. 9C and 9D depict navigation and traversal of the hierarchies ofFIGS. 9A and 9B;

FIG. 10A is a class diagram for weather events in accordance with theintelligent broadcast system of FIG. 1;

FIG. 10B is a class diagram for traffic events in accordance with theintelligent broadcast system of FIG. 1;

FIG. 10C is a class diagram for weather events in accordance with theintelligent broadcast system of FIG. 1;

FIG. 10D is a class diagram for presentation elements in accordance withthe intelligent broadcast system of FIG. 1;

FIG. 10E is a class diagram for templates in accordance with theintelligent broadcast system of FIG. 1;

FIG. 11 is an activity diagram for the identification of events andgeneration of corresponding datasets in accordance with the intelligentbroadcast system of FIG. 1;

FIG. 12 is an activity diagram for the selection of events andnavigation of presentations in accordance with the intelligent broadcastsystem of FIG. 1;

FIG. 13 is a use case diagram of a convective storm analysis system inaccordance with one embodiment of the present method and system and thatcan be interfaced with the intelligent broadcast system of FIG. 1;

FIG. 14 is a system diagram for a weather severity and characterizationsystem in accordance with one embodiment of the present invention;

FIG. 15 is a use case diagram in accordance with the weather severityand characterization system of FIG. 14;

FIG. 16 is a flow diagram in accordance with the weather severity andcharacterization system of FIG. 14;

FIG. 17 depicts the temporal evolution of two storms in accordance withthe weather severity and characterization system of FIG. 14;

FIG. 18 shows a partial graphical representation of a multi-dimensionaldatabase in accordance with the weather severity and characterizationsystem of FIG. 14;

FIG. 19 shows a partial graphical representation of a multi-dimensionaldatabase in accordance with the weather severity and characterizationsystem of FIG. 14;

FIG. 20 demonstrates duplication of data from a single instance of astorm in the multi-dimensional database due to identification frommultiple sources of weather data in accordance with the weather severityand characterization system of FIG. 14;

FIG. 21 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 22 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 23 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 24 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 25 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 26 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 27 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 28 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 29 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 30 is an exemplary graphical user interface in accordance with theweather severity and characterization system of FIG. 14;

FIG. 31 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 32 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 33 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 34 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 35 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14;

FIG. 36 is an exemplary graphical user interface in accordance with theweather severity and characterization system of FIG. 14; and

FIG. 37 is an exemplary graphical presentation in accordance with theweather severity and characterization system of FIG. 14.

DETAILED DESCRIPTION OF THE INVENTION

Certain terminology is used herein for convenience only and is not to betaken as a limitation on the present invention. In the drawings, thesame reference letters are employed for designating the same elementsthroughout the several figures.

The words “right”, “left”, “lower” and “upper” designate directions inthe drawings to which reference is made. The words “inwardly” and“outwardly” refer to directions toward and away from, respectively, thegeometric center of the intelligent broadcast system 100 and designatedparts thereof. The terminology includes the words above specificallymentioned, derivatives thereof and words of similar import.

The intelligent broadcast system 100 of the present invention permits apresenter, such as a weather or traffic broadcaster or reporter toautomatically determine, isolate and present interesting aspects orevents within a set of data. The interesting aspects or events may bedetermined according to the preferences of the reporter (e.g., what thereporter thinks is interesting), or may be determined according topre-determined guidelines related to the data (e.g., a particular classor type of event that is generally considered interesting). Such eventsor aspects might include, for example, with respect to weather data,record high temperatures, eventful storms (e.g., thunderstorms,hurricanes, etc.), or significant historical events. With respect totraffic data, such events might include an accident, known congestedareas or construction zones. Those skilled in the art will recognizethat the aspects or events need not actually be significant occurrencesto be identified as an aspect or event by the intelligent broadcastsystem 100. That is, the interesting aspects or events that areidentified may, in fact, occur without the presence of a significantevent. For example, with respect to weather data, on an average day whenthere are no thunderstorms, record temperatures, etc, in a geographicregion of weather interest, the weather broadcaster may nonetheless beinterested in presenting average aspects or events about the weather,even though there are no major events to display.

In one embodiment, the presenter 102 is able to assign a precedence orpriority to events or types of events, such that the intelligentbroadcast system 100 automatically prioritizes the events that areidentified and/or identifies events that have a certain priority. Forexample, rules that identify severe weather in the immediate vicinity ofthe presenter's broadcast location may have a high priority, whereasrules that identify weather events for a remote location may have alower priority. Alternatively, tornados (irrespective of geographiclocation) may have a higher priority than a non-eventful rainstorm.

In one embodiment, the presenter 102 may desire to display an event mapthat includes a maximum number X of events. However, if, according tothe rules established by the editor 150 or the presenter 102, theintelligent broadcast system 100 identifies a number or events greaterthan X, then the system would only present for selection (i.e., displayon a selection map or other graphical display) those X events having thehighest assigned priority. Alternatively, the presenter 102 couldestablish a rule that identifies and/or displays events only of specificpriorities. Those skilled in the art will recognize that the priorityassigned to a particular event or event type could be assigned by theeditor 150, presenter 102 or be a default priority assigned by theintelligent broadcast system 100.

In another embodiment, events are identified according to regionalinterests as determined by pre-defined default conditions and/or thepresenter 102 or editor 150. For example, even though a particular eventis identified by the system according to the rules generally, such anevent may actually be applicable (e.g., relevant) to a particularregion, city or group of people (e.g., people in Boston are likely notinterested in record high temperatures in Phoenix, while they may beinterested in the current snowfall in Philadelphia). Accordingly, eventhough an event is identified by the system, it may not actually bepresented for selection depending on its regional or local relevance.Such a regional filter could be used in conjunction with the eventpriorities discussed above.

Once the intelligent broadcast system 100 has identified the events ofinterest, presentation elements corresponding to those events aregenerated by the intelligent broadcast system 100. Depending on thepresenter's preferences or rules, the presentation elements include, forexample, graphics, video, audio and/or text, etc., data about theidentified events. The presentation elements may also includeinformation or data related to associated events. The identified eventsare preferably displayed to the user in the form of markers, links oricons, such that the reporter is able to select a desired event.Selection of an event initiates a presentation comprising thepresentation elements and according to the presenter's rules. Thepresenter has the ability to navigate through the presentation in anorganized, hierarchical manner related to the selected event.

Thus, in the intelligent broadcast system 100, the user has the abilityto determine what types of data (e.g., events) are interesting,determine what types of data related to the identified event(s) shouldbe assembled and/or associated with certain other types of data forpresentation purposes, and instruct the system to present that data inan interesting or particular manner. The intelligent broadcast systemthus automatically finds such data and upon selection of interestingfeatures, events or aspects of that data, displays and allows for thepresenter to navigate through prepared presentations related to thatdata.

The intelligent broadcast system 100 is described herein primarily withreference to weather data and weather reporting and broadcasting.However, those skilled in the art will recognize that the intelligentbroadcast system according to the present invention is applicable toother forms of data and presentation thereof, including traffic data,economic and business analyses, political news, environmentalmonitoring, transportation and logistics, military operations, andgenerally to any other form of data and/or reporting that can bereferenced and reported in an organized manner.

FIG. 1 illustrates a Unified Modeling Language (“UML”) use-case diagramfor an intelligent broadcast system 100 and associated systems andactors in accordance with the present method and system. UML can be usedto model and/or describe methods and systems and provide the basis forbetter understanding their functionality and internal operation as wellas describing interfaces with external components, systems and peopleusing standardized notation. When used herein, UML diagrams including,but not limited to, use case diagrams, class diagrams and activitydiagrams, are meant to serve as an aid in describing the present methodand system, but do not constrain its implementation to any particularhardware or software embodiments. Unless otherwise noted, the notationused with respect to the UML diagrams contained herein is consistentwith UML 2.0 specification or variants thereof and is understood bythose skilled in the art.

Referring to FIG. 1, a presenter 102 and/or an editor/producer 150preferably interact with the intelligent broadcast system 100 throughthe use of a display/monitor system 104, and a human-machine interface(not shown). Display mechanisms and human-machine interfaces aregenerally known in the art, and a description thereof is omitted hereinfor convenience only, and should not be considered limiting. Thoseskilled in the art will further recognize that the presenter 102 and theeditor/producer 150 may be the same or different person or groups ofpersons. Furthermore, the presenter 102 and/or the editor/producer 150may be an entity or computer system or network designed to interact withthe intelligent broadcast system 100.

Referring to the right hand side of FIG. 1, the intelligent broadcastsystem 100 includes an obtain rules use case 124 that the intelligentbroadcast system 100 utilizes to retrieve and organize rules to beapplied to the relevant data, such that the intelligent broadcast system100 may determine relevant event(s) of interest, appropriate informationor data to display corresponding to those events and the desired mannerof such display(s).

The obtain rules use case 124 accesses and retrieves the desired rulesto be applied to the relevant data through a create rules use case 126and/or a rules database 120. The create rules use case 126 permits theeditor/producer 150 to create specific or general rules relevant to theparticular situation or type of presentation(s) that are desired orrelevant to the type of data or situation that is going to be analyzed.The create rules use case 126 and the obtain rules use case 124preferably include a human interface such as a text editor, GraphicalUser Interface (GUI), form based entry, or other data entry mechanismgenerally known to those skilled in the art, that enables theeditor/producer 150 to interact with the intelligent broadcasts system100 to create, update and/or select rules for creation of the desiredpresentation(s).

In an alternate embodiment, presenter 102 creates rules for use inconjunction with the presentation. Thus, those skilled in the art willunderstand that creation, editing and selection of the desired rules maybe accomplished by one or a combination of people, and maybe undertakenat the site of the presenter 102, editor 150 and/or a remote locationand transmitted to the presenter 102 or editor 150.

Similarly, the rules database 120 preferably includes rules that havebeen previously created and stored by the editor/producer 150 (or adifferent editor/producer 150). The rules in the rules database 120 maybe reviewed and retrieved according to the desired presentation type oranalysis. Thus, the obtain rules use case 124 permits theeditor/producer 150 to select which pre-stored rules from the rulesdatabase 120 are to be utilized in generating the presentation(s) forthe intelligent broadcast system 100. The update rules use case 122enables rules that are created by the editor/producer 150 in the createrules use case 126 to be stored in the rules database 120 or updates theexisting rules that have been previously stored in the database 120 forfuture use. Discussion and examples of the rules that may be created,updated and/or stored in the rules database 120 or obtained through theobtain rules use case 124 are discussed in greater detail below.

The intelligent broadcast system 100 further includes an apply rules usecase 128, through which the rules obtained in obtain rules use case 124are applied to the relevant data to be analyzed for presentation. Therules are preferably applied to current data provided by a current datasystem 140 and/or historical data provided by a historical data system130. Examples of current and historical data include surface weatherobservations, radar imagery, lightening data, satellite imagery,climatological normals, record maximums and minimums or highs and lows(of various data types, such as temperature, precipitation, etc.) andother notable weather events. Those skilled in the art will recognizethat the rules may also be applied to predicted or forecasted aspects ofsuch data. The application of the rules to the relevant data permitsevents or aspects within that data that are of interest (as defined bythe applied rules) to be determined or otherwise identified through theidentify events/aspects use case 132.

A generate presentation elements use case 118 determines, obtains andgenerates data representations (data or sets of data) corresponding tothe events or aspects identified by the identify events/aspects use case132. That is, the generate presentation elements use case 118 accessesdata relevant to the identified events and aspects (for example, fromthe current and historical data systems 130, 140) and, in accordancewith the rules used in the apply rules use case 128, including thoserules used to identify the aspects or events themselves, compiles thedata representations presentation elements, information, and other datacorresponding to the rules for the particular identified events oraspects. The presentation elements may be derived from any data or setof data (e.g. the data representations) in a variety of formats. Thecontent and format of the presentation elements is defined by theapplied rules, as discussed in greater detail below.

The generate presentations use case 118 also assembles presentationsrelated to the identified events or aspects for selection and/orpresentation by the presenter 102. The generated presentations includedata representations and presentation elements corresponding to theidentified events or aspects. That is, the data representationscorresponding to the events identified by the identify events/aspectsuse case 132 are used to generate presentation elements related to thoseevents in accordance with the rules. The presentation elements andpresentations as defined by the rules may by any combination ofgraphics, video, text, audio or any other display or presentationmechanism or format generally known to those skilled in the art.

As described above, the rules used by the apply rules use case 128facilitate the identification of events, formation of presentationelements, and production and navigation of presentations andpresentation elements corresponding to the relevant current andhistorical data utilized by the intelligent broadcast system 100.

A number of functions can be defined which serve as the basis forexpressions that serve as rules. With respect to the identification ofevents and formation of presentation elements, the following arerepresentative functions that can be used to locate events of interest,identify those events, and form corresponding presentation elements:

-   -   DAMAGE(location, threshold)    -   EXTREMES(location, parameter type, lower limit, upper limit)    -   RECORD(location, date range)    -   STORM(location, time)    -   DELAY(location, delay)

The functions may include a location field that may be described throughthe use of standardized abbreviations such as city indicators (BOS),geographic region indicators (US), full city names (Boston), stateabbreviations, or other predefined geographic indicators. Similarly,time can be indicated through traditional representations of date andtime (Nov. 4, 2005, 12:00) as well as representations that encompassgeneral time spans (a.m., p.m., today, current). In one embodiment, therepresentations that encompass general time spans are given explicitdefinitions and allow a human to use easily recognizable words such as“today” which will have a specific predefined meaning (e.g. within thelast 12 hours). Other mechanisms for the representation of locations,time ranges, and other parameters generally known to those skilled inthe art may be used. By predefining the particular mechanism forrepresenting variables, the editor/producer 150 can use commonlyrecognizable language to identify specific locations, times, and otherparameters associated with an applications such as weather and traffic.

The functions may be combined with Boolean operators, well knownconditional statements such as IF . . . THEN, and search commands suchas FIND, to produce rules or sets of rules applicable to datamonitoring, formation of presentation elements and presentations andnavigation thereof.

Preferably, data monitoring rules search current, historical and/orforecast data looking for and identifying events of interest (as definedby the rules, and thus the editor/producer 150). For example, a rule maysearch through current weather data for the entire United Sates lookingfor cities that have recorded a record high temperature for that day. Ofcourse, such searching will also incorporate analysis of the historicalweather day for that particular day, since previous record hightemperatures must be ascertained to determine what the previous recordis, as well as predicted or forecast data. In this case, the rules willidentify cities that have recorded a record high temperature.

Once the data monitoring rules have identified an event of interest(e.g., record high temperature), data representations, presentationelements and other data corresponding to that event are generatedaccording to the designated rule. That is, data representations can bedetermined based on the identified events. The data representations willcontain information (or indices or pointers to information) that willallow corresponding presentation elements to be prepared if the event ofinterest is selected. For example, in the case of record hightemperatures, corresponding presentation elements might include alisting of all of the other cities within a 50 mile radius that alsohave recorded record high temperatures.

A representative rule for monitoring record temperatures in the US,identifying those events in the database and on the current (today's)weather map, and creating presentation elements with relevantinformation regarding that event is:

-   -   IF Temperature(US, today)>PastHighTemperature(US, today)        -   OR    -   IF Temperature(US, today)<PastLowTemperature(US, today),        -   THEN            -   DECLARE_EVENT(US, today, record_temp, location)        -   AND            -   FORM_PRESENTATION_ELEMENT(record_temp, location,                records).

Other examples of rules for data monitoring, identification of eventsand generation of presentation elements include:

-   -   1. IF (daily rainfall at Logan Airport>0.2 inches for more than        5 {n} days),        -   THEN            -   DECLARE_EVENT (Logan Airport, rainfall)        -   AND            -   FORM_PRESENTATION_ELEMENT (Boston metro graphic that                shows {n} day rainfall totals at 7 reporting stations in                the Boston metro area).    -   2. IF (maximum temperature at Logan Airport today is at least 20        degrees warmer than yesterday),        -   THEN            -   DECLARE_EVENT (Logan Airport, today, yesterday,                max_temp)        -   AND            -   FORM_PRESENTATION_ELEMENT (Boston metro graphic with                contours of 24 hour temperature change)        -   AND            -   FORM_PRESENTATION_ELEMENT (NE region 24 hour animation                of frontal positions, highs, lows, and jetstream)    -   3. IF (hurricane center within 200 miles of US coastline),        -   THEN            -   DECLARE_EVENT (hurricane, location)        -   AND            -   FORM_PRESENTATION_ELEMENT (48 hour satellite loop                centered on the hurricane center 12 hours ago)        -   AND            -   FORM_PRESENTATION_ELEMENT (48 hour forecast hurricane                track animation over a region centered two thirds of the                way between current hurricane center and forecasted                landfall)

In one embodiment, additional rules may be used to form presentationelements related to historical record temperatures, such as videofootage of conditions related to previous historical records (e.g.footage of a drought, heat wave, etc.). An exemplary rule that would beappended to the above rule for monitoring record temperatures might be:

-   -   FORM_PRESENTATION_ELEMENT(record_temp,        historical_weather_event).

Similarly, in another embodiment, a rule might be developed to monitorboth weather and traffic conditions in a particular location orlocations that naturally correspond to one another, for example, Bostonand Cape Cod. Such a rule would be particularly useful for monitoringconditions that could lead to traffic slowdowns during heavy traffictimes, such as on summer weekends. A representative rule might be:

-   -   IF Precipitation({BOS, Cape Cod}, {Friday AM, Sunday AM})>0.2        inches;        -   OR    -   IF ForecastPrecipitation({BOS, Cape Cod}, {Friday, Saturday,        Sunday})>0.2 inches;        -   AND    -   IF TrafficConditionsDelay({BOS, Cape Cod}, {Friday, Saturday,        Sunday})=TRUE;        -   THEN            -   DECLARE_EVENT(BOS, today, traffic_advisory);        -   AND            -   FORM_PRESENTATION_ELEMENT(traffic_advisory,                traffic_conditions).

As will be understood by those skilled in the art, rules can bedeveloped to monitor current conditions for events of interest and torelate those current events to past events or conditions. Byautomatically identifying events of interest, it is no longer necessaryto have a human monitor all of the incoming events to determine whatevents are potentially of interest to the audience. That is, the eventsof interest are automatically determined according to the rules selectedor created by the editor/producer 150 of the intelligent broadcastsystem 100.

In another embodiment the presenter 102 has the ability to manuallyidentify a geographic region of interest. For example, the presenter 102could select or highlight a geographic area of interest on a weather mapthat may or may not already include identified events. Based onpre-defined rules, the intelligent broadcast system 100 could thenevaluate the data corresponding to the geographic area highlighted bythe presenter 102 and identify events and generate presentation elementsassociated with that area, as described herein.

Similar to the data monitoring rules described above, the intelligentbroadcast system 100 allows for functions or rules to be defined thatgovern the display, selection and presentation of identified events andthe presentation of the presentation elements associated with a selectedevent. Such presentation/navigation rules may be incorporated with orseparate from the data monitoring and event identification rulesdiscussed above.

Examples of the types of functions utilized with thepresentation/navigation rules include:

-   -   DISPLAY(presentation element, template, screen location, time,        entrance, exit)    -   RETURN(presentation elements, return level)    -   SEQUENCE(presentation elements, templates, screen location,        timing)    -   FORCE_LATERAL(presentation elements, timing)    -   AUTOMATIC_SEQUENCE(presentation elements, timing)    -   CUTOFF(presentation elements, cutoff time)

In one embodiment, once events of interest have been identified andcorresponding presentation elements formed, the presenter 102 has theopportunity to select one or more of the identified events and have thepresentation and/or additional materials related to that event displayedaccording to the rules that initially identified the event and itscorresponding presentation element(s). The related materials may havealready been prepared, in the sense that there is no additionalsearching or preparation of presentation elements involved.Alternatively, the materials to be presented may be prepared accordingto additional rules or input from the presenter 102.

In one embodiment, the events identified by the identify events use case132 are identified as such for the presenter 102 on the display/monitorsystem 104. For example, the presenter 102 may view a display, such as aweather map or other graphical display of data, with identified eventscorresponding to that data (as identified by the applied rules) markedon the display. The identified events or aspects may be identified ormarked with an icon, link or other graphical or text marking mechanismgenerally known in the art. Those skilled in the art will understandthat the type of marker used to mark or present the identified events tothe presenter 102 need not be representative of the actual event beingmarked. For example, a blinking dot could be used to denote activethunderstorms on a weather map instead of more traditional weathermarking.

A select events use case 110 enables the presenter 102 to select orhighlight a particular event of interest to receive an automaticpresentation of materials related to that event. The desired event(s)may be selected any one or combination of selection methods generallyknown in the art, including, pointing and clicking, highlighting, voice,touch screen technology, keyboard entry, etc.

Selection of a particular event by the presenter 102 allows presentationelements produced by the generate presentations use case 118 to bepresented on the display/monitor system 104. The presenter 102 may alsoutilize the display/monitor system 104 to view and navigate through thepresentation elements. Exemplary rules for the presentation andnavigation display of record temperatures associated with an identifiedevent on the display indicating a record temperature in a particularcity might be:

-   -   IF SELECT(record_temp, location)=TRUE        -   THEN            -   DISPLAY(presentation_materials, record_temp, location).

This rule would, upon selection of an identified record temperatureevent by the presenter 102, cause the presentation elements related tothe selected record temperature to be displayed and/or navigated by thepresenter 102.

The manner in which presentation elements are presented may also bespecified to the intelligent broadcast system 100. For example, it maybe desirable to have the presentation elements formatted according tothe records template (e.g., as described in records template class 442),presented in the upper left portion of the display, time thepresentation on command (e.g. mouse click or other command), and havethe information associated with the selected event enter from the leftand fade on command.

The presentation/navigation rules can be employed to allow the presenter102 to navigate through a hierarchy of information associated with aselected event. That is, the presentation elements associated with anevent may inherently or explicitly include a hierarchy of thosepresentation elements, such that some of those presentation elements arelinked together and/or intended for display in a particular orderrelative to the other presentation elements associated with that event.Moreover, a hierarchy of presentation elements may also includepresentation elements that are related to other, non-selected events ormay include the option to select additional events.

Navigation through the presentation elements and their related datarepresentations may include access to additional presentation elementsand display of additional information related to the selected event,force the presenter through certain sequences of information related tothe selected event or allow or force a return to the main display (i.e.,not display any additional information). Alternatively, thepresentation/navigation rules could enable or force the display ofadditional materials in the hierarchy. For example, selection of aninitial event and presentation of material corresponding thereto, maylead to a display that presents other identified events of interest forselection. In such a case, the navigation rules may permit anotheridentified event to be selected, such that subsequently presentedinformation corresponds to the second selected event instead of thefirst selected event (although in substance the material may berelated). Navigation rules may also limit the amount of time spent onany one portion or display within the presentation, or cause otheractions to take place on an automated basis. Alternatively, navigationrules can be put in place allowing the user more flexibility innavigating the hierarchy with fewer constraints.

As an example, a set of navigation rules to control progression througha hierarchy of information (such as record temperatures) might be:

-   -   IF remaining_presentation_time<30 s        -   THEN            -   RETURN(records, top).

This rule would, if the presentation time is less than 30 seconds, forcethe presenter to the top of the hierarchy upon completion of thedescription of the record temperature related information.

In one embodiment presentation/navigation rules are used to control thedisplay and sequencing of the additional materials in the hierarchy. Adifferent rule can be used to cause the auto-sequenced display ofinformation related to traffic conditions:

-   -   IF traffic_advisory        -   THEN            -   AUTOSEQUENCE (alternate routes).

This rule would cause an automatic sequencing of the alternate routes tobe displayed (as in autosequence 386 of FIG. 9C.)

Other presentation/navigation rules can be developed and implemented tocontrol or constrain the flow of the presentation. In one embodiment thepresentation/navigation rules are automatically modified based on one ormore parameters including the number of events selected by thepresenter, the depth of the hierarchy already explored, or the remainingtime in the presentation. In this embodiment the modification of thenavigation rules takes place based on the current conditions of thebroadcast and events (selections) made during the broadcast, in additionto program constraints.

As noted above, individual rule sets can be created to accomplish datamonitoring, formation of presentation elements, and creation andnavigation of those presentation elements in accordance with the desiresof the editor/producer 150 and/or the presenter 102. Alternatively,general sets of rules can be generated to accomplish the aforementionedtasks.

Presenter 102 may also navigate the a predefined hierarchy ofpresentation sequences or displays corresponding to the selected eventusing the increase level use case 112 and/or the decrease level use case114. Similarly, the presenter 102 may return to the top level (e.g., theinitial event selection display) through return to top level use case116.

Referring to FIGS. 2-8, an exemplary presentation that may be presentedby the intelligent broadcast system 100 is shown. FIG. 2 illustrates anexample of a graphical display (in this case a typical map showingcurrent weather conditions across the United States) 200 that includesidentified events or aspects that are displayed to the presenter 102. InFIG. 2, the generate presentations use case 118, in conjunction with theidentify events use case 132, has identified the weather conditions thatmeet the conditions set forth by the data monitoring rules obtained bythe obtain rules use case 124. The identified events in FIG. 2 aredenoted by markers or icons 202, 204, 206 that populate the graphicaldisplay 200. In this example, the graphical display 200 serves as the“home” display of the presentation, discussed in greater detail below.The markers 202, 204, 206 thus indicate geographic locations or regionswhere an aspect, event or region of interest exists, as determined bythe applied rules. As described above, presentation elements may beassociated with each of the markers 202, 204, 206, as generated bygenerate presentation element use case 118 in accordance with the rules.Similarly, presentation elements (or data representations correspondingto the presentation elements) may be available corresponding to theevents represented by each of the markers 202, 204, 206. Those skilledin the art will recognize that presentation elements and correspondingpresentations need not be available for each of the markers (i.e.,aspects or events) identified on the graphical display 200. Rather, itmay be the case that the editor/producer 150 defined rules to preparepresentations for only aspects or events that meet certain criteria,even though the data monitoring rules were defined to include othersimilar types of events. For example, it may be the case that thepresenter 102 desires to identify the cities/regions having record hightemperatures, but only needs a presentation for such identified events(cities/regions) in the eastern portion of the U.S.

In an alternative embodiment, the data sets and/or the presentationelements associated with a marker or identified event are dynamicallyformed and prepared upon selection of a particular marker.

A presenter 102 interacting with the graphic display 200 has the optionto select any of the markers 202, 204, 206 through the select events usecase 110. For example, selecting the marker 202, may retrieve apresentation that comprises sequential presentation elements such asthose shown in FIGS. 3 and 4. Thus, selecting marker 202 displays anenlarged graphic 220 depicting Southern California. In this example, themarker 202 remains in the display 220, such that the user may selectanother action within the presentation. However, it is understood thatthe display 220 could include additional markers (e.g., events ofinterest) that did not appear on the initial display 200. Alternatively,the display 220 need not include any additional markers to events if therules so define.

In the display 220 of FIG. 3, the exemplary template rules within thepresentation element corresponding to event/marker 202 have defined thata storm track 224 is to be shown that illustrates the path of the stormfrom the Pacific Ocean toward Los Angeles. The display 220 furtherincludes a return button 230 that allows the presenter 102 to exit thedata set and return to the home graphic display 200 of FIG. 2.Similarly, selecting the marker 202 in FIG. 3 advances the presentationto a graphic display 222 in FIG. 4, showing a yet more detailed radarimage of Los Angeles. Selecting the return button 230 in FIG. 4 returnsthe presentation to the graphic display 200 of FIG. 2.

Referring again to FIG. 3, if the presenter selects marker 206(indicating a condition of interest in the Northeast), an exemplarygraphical sequence 240 (see FIG. 5), defined by the presentation elementcorresponding to the marker/event 206, is presented. The initial graphicto be displayed (A) highlights the condition of interest—record coldtemperature. The sequence next displays a video clip (B). Suchsequencing may be triggered by user interaction or be an autosequenceusing predetermined timing. A marker 242 is presented, which, ifselected, skips the video clip (B) and advances the sequence to thetabular graphic (C) that illustrates the record cold temperatures invarious cities in the Northeast. Each of the graphics in the sequence240 includes a return button 230 that enables the presenter 102 toreturn the presentation to the initial graphical display 200, shown inFIG. 2. Selecting a marker 244 from the graphic (C) advances thepresentation to a presentation element that provides greater detailabout Boston. As shown in FIG. 6, an initial graphic 250 is providedshowing the current conditions in Boston. The return button 230 in thegraphic 250 will return the visualization to the graphic (C) in FIG. 5.However, selecting the “next” icon 252 advances the presentation to thegraphic 260 (see FIG. 7) that shows a weather map of the greater Bostonarea. In FIG. 7, a marker 262 indicates that there is a condition ofinterest at Boston's Logan Airport. Upon a selection of the marker 262,the presentation advances to the graphic 270 (see FIG. 8) that showsairport delays 272 for Boston's airport. Selecting the return button 230in either of graphics 260 or 270 returns the presentation to the graphicdisplay (C) in the sequence 240 of FIG. 5.

As previously described, the intelligent broadcast system 100 can beused to enhance weather presentations through the automatic marking ofevents of interest, and by providing presenter 102 with the ability toaccess additional information through a selection process. By providinginformation in a hierarchical format, presenter 102 can use theintelligent broadcast system 100 to explore additional informationrelated to current weather events. By having the additional informationin a hierarchical configuration presenter 102 can explore informationrelated to a marked event at a shallow level or can continue adiscussion regarding a particular marked event by exploring the deeperlevels of the hierarchy. The incorporation of historical data into theweather presentation provides presenter 102 with additional materialthat is potentially of interest and which may be necessary during timesof “uninteresting” current weather.

FIG. 9A illustrates a representative hierarchical set of potentialpresentation elements related to weather (current and historical) thatcould be generated by a set of rules and made available to a presenter102 during a dynamically formed presentation or broadcast. In oneembodiment, a weather map contains a number of marked events or regionsof interest which, upon selection, cause the generation or retrieval ofpresentation elements related to that event/region of interest, asdescribed above, for example, with respect to FIG. 2). In some cases,the presenter is presented with icons or other indicators which give thepresenter the ability to select additional events/regions of interest.In FIG. 9A, an event/region of interest 300 can be selected and, inaddition to the presentation elements associated with event/region ofinterest 300, the presenter is given the opportunity to navigate tocurrent weather conditions of interest 302 which are related toevent/region of interest 300, or to select historical weather conditionsof interest 312, that are also related to event/region of interest 300.

If the current weather conditions of interest 302 are selected, thepresenter 102 will have the ability to go further into the hierarchy andto display information related to storm tracking 304, hazardousconditions 306, weather related news 308, and extremes 310. Ifhistorical weather conditions of interest 312 is selected, the presenter102 will have the ability to go further into the hierarchy and todisplay news records 314, historic weather related news 316, orstatistics and trends 318. Although a number of categories of weatherrelated conditions, news and items of historical interest have beendescribed with respect to FIG. 9A, other categories or types of itemsmay be used to create materials for presentation in the weatherhierarchy. Furthermore, those skilled in the art will recognize that ahierarchy generated in accordance with the intelligent broadcast system100 need not include the hierarchical structure and presentationelements shown in FIG. 9A. That is, the particular presentation elementsand hierarchy available to a presenter for display and navigation mayvary greatly depending on the set of rules applied to the relevant data.

As previously discussed, the intelligent broadcast system 100 isapplicable to traffic information and in particular, traffic conditionseither in conjunction with a weather presentation, in the context of astand-alone traffic presentation, or as part of another type ofpresentation which incorporates traffic information. For example, apresenter may discuss weather conditions in a certain metropolitan areawhere those weather conditions led to a traffic slowdown in thatmetropolitan area. In accordance with the present invention, theintelligent broadcast system 100 will have created a presentationelement associated with the traffic slowdown and presented the trafficslowdown as a marked event of interest on the weather map. Uponaccessing the mark corresponding to the traffic slowdown, additionalinformation related to that traffic condition, including the cause ofthe slowdown, expected duration of the slowdown, and alternate routescan be displayed. As previously described, an autosequence may beutilized to present a series of traffic information presentationelements, or the presenter may manually access additional presentationelements. The traffic related presentation elements may be laterallyorganized or may be at different levels in the hierarchy as illustratedin FIG. 9B. As previously described, navigation rules may constrain thepresenter based on a number of conditions including the number of otherevents, number of events previously explored by the presenter, andremaining time in the broadcast.

FIG. 9B illustrates a representative hierarchical set of presentationelements related to traffic that can be used by a presenter 102 during adynamically formed broadcast. In one embodiment, the presenter 102initiates the presentation with the same weather map used as the basisfor the presentation of weather as previously discussed with respect toFIG. 9A. Thus, the event/region of interest 300 serves as the entrypoint into the hierarchy for presentation of traffic relatedinformation. Traffic conditions of interest 350 can be displayed, andthe presenter 102 has the opportunity to go further into the hierarchyto access presentation elements related to items such as excessivedelays 352, hazardous traffic conditions 354, and detours/closed routes356. With respect to detours/closed routes 356, the hierarchy maycontinue with the ability to present material related to alternate route#1 358 as well as alternate route #2 359.

In an alternate embodiment, a separate traffic related map (not shown)is used as the basis for discussing traffic. In such a presentation,traffic related events/regions of interest are marked on the traffic maprather than on the weather map. Once entry into the hierarchy isinitiated, it continues as described with respect to FIG. 9B.

FIG. 9C illustrates a navigation sequence for a hierarchy of informationin which the presenter 102, upon entering the presentation hierarchyfrom a home display 380, traverses the presentation along a first leg382, a second leg 384, an autosequence 386, followed by a return 388 tothe home display 380. In this example, the presenter 102 navigatesthrough first and second legs 382, 384 by choice but is guided throughthe autosequence 386 and the return leg 388 automatically in accordancewith the navigation rules.

FIG. 9D illustrates an alternate navigation sequence in which thepresenter 102 enters the presentation hierarchy from the home display380, traverses the presentation along a first leg 382, and then selectsa first lateral leg 390 followed by a second lateral leg 392. A thirdleg 394 traverses the presentation to the bottom of the hierarchy,followed by return 388 to the home display 380.

FIGS. 10A-10E illustrate class diagrams for events, presentationelements and templates, and show attributes and operations associatedwith each class or metaclass. The class diagrams shown in FIGS. 10A-10Eare consistent with those used in UML and serve to better describe andillustrate the methods and systems associated with the intelligentbroadcast system 100, but do not constrain the implementation of thosemethods and systems to a particular implementation or computing system,operating system, programming language, or design architecture.

FIGS. 10A-10C illustrate types of classes that can be used to index,store, organize and manipulate data associated with traffic and weather.The use of classes does not constrain the types of data to be indexed,stored, organized or manipulated, nor does it limit the mechanisms bywhich that data is managed. For example, in one embodiment, a relationaldatabase may be employed to store all of the weather and traffic relatedinformation, while in an alternate embodiment an object orienteddatabase is used. By describing the particular attributes and operationswhich can be performed on certain types of data it is possible to createmechanisms for searching the data for events of interest, identify thoseevents of interest, and subsequently form presentation elements whichcan be used to develop presentation elements, as will be describedherein.

Referring to FIG. 10A a weather conditions metaclass 400 is definedwhich contains a number of basic attributes and operations associatedwith a weather condition. Classes associated with weather conditionsmetaclass 400 can include, but are not limited to, temperature class402, precipitation class 404, winds class 406, and humidity class 408.For each of these classes, particular attributes and operations may bedefined as are applicable to that class. For example, a directionattribute can be associated with winds class 406, but will not beapplicable to temperature, precipitation, or humidity.

FIG. 10B illustrates a number of classes associated with traffic,including a traffic conditions metaclass 410, an accidents class 412, aconstruction class 414 and a slowdown class 416.

FIG. 10C illustrates a number of classes associated with severe weather,including a severe weather metaclass 420, a thunderstorms class 422, asnowstorms class 424 and a hurricane class 426.

FIG. 10D illustrates exemplary classes that can be defined to describepresentation elements. Presentation elements comprise aggregatedinformation developed based on particular events and that can be readilyconverted into presentation materials through the use of datarepresentations, presentation templates or by means of other code thatidentifies and/or generates data or other materials for display.Presentation elements or datasets need not be separately or explicitlystored data, but can be realized as a set of links or pointers to datacontained within a database or other data storage structure or device.As shown in FIG. 10D, a presentation elements metaclass 430 includesrecords presentation elements 432, predictions presentation elements434, storm presentation elements 436 and historical weather eventpresentation elements 438. As an example of the use of presentationelements, records presentation elements 432 can be utilized to obtain arecord temperature or (high or low) and the corresponding time andlocation for the record. By storing or indexing the record location,date and type, presentation elements describing the record temperatureassociated with an event/region of interest can be readily formed whenthat material is required by the presenter, or in some instances, justprior to presentation.

FIG. 10E illustrates exemplary classes that can be used to definetemplates. Templates provide the basis for formatting, rendering anddisplaying (producing) information in the intelligent broadcast system100. In one embodiment, the material to which the template is appliedcomes from a corresponding presentation element or event. In analternate embodiment, no presentation elements are used and separatedata gathering or mining is performed to access data against which thetemplate can be applied. As shown in FIG. 10E, a templates metaclass 440is associated with a records template class 442, a predictions templateclass 444, a storm data template class 446 and a historical weatherevent template class 448. The templates illustrated in FIG. 10E are usedto create overlays, segments of video, graphics, or other materialswhich present the information of interest within the hierarchy ofpresentation elements.

The rules discussed above with respect to FIG. 1 can be used inconjunction with the classes shown in FIGS. 10A-10E to accomplish eventidentification, dataset formation, and generation of presentationelements, although other classes, objects, or data structures can beused to accomplish those tasks.

Exemplary operation of the intelligent broadcast system 100 is discussedwith respect to the activity diagrams of FIGS. 11 and 12. As shown inFIG. 11, rules are obtained in an obtain data monitoring rule step 500and are subsequently applied to both current and/or historical data inan apply data monitoring rules to current data step 510 and an applydata monitoring rules to historical data step 520, respectively,depending on the rule being applied. As discussed above, it is notrequired for both current and historical data to be referenced and/oranalyzed in identifying events and/or generating presentations. Eventsare subsequently identified in an identify event step 530 andpresentation elements can be generated in a generate presentationelements step 540.

Referring to FIG. 12, the process of presentation and navigation can beunderstood as beginning with a receive event selection step 550 followedby a retrieve presentation elements step 560. Based on the retrievedpresentation elements, a display and navigate presentation step 570 isexecuted followed by an additional elements test 590 to determine ifthere are more items to be displayed as part of the presentation. If theend of the presentation has not been reached, an apply next navigationrule step 595 is executed and the system returns to the display andnavigate presentation step 570.

Within the context of FIG. 12, the receive event selection step 550includes receiving navigation commands including commands to advance toitems further down in the hierarchy (as represented by increase leveluse case 112), higher up in the hierarchy (as represented by decreaselevel use case 114), or to return to the top level in the hierarchy (asrepresented by return to top level use case 116). Rules causing anautomatic return to a previous level or top level will cause anautomatic return not illustrated in FIG. 12.

In use, the intelligent broadcast system 100 can be operated bypresenter 102, with the help of an assistant, or by the editor/producer150 through a number of human-machine interfaces common to broadcastsystems. These interfaces include, but are not limited to, remotecontrols including wired, wireless or optical remotes (includinghand-held controls and foot pedals), on-display selection including handtracking systems, touch sensitive displays or screens, or othermechanisms suitable for sensing the presenter's selection of an event orlocation.

A keyboard/mouse can be utilized by the presenter 102, an assistant, orthe editor/producer 150 to select events or marked locations at thedirection of presenter 102. These events may be selected from the samedisplay/monitor 104 being utilized by presenter 102 and visible to theaudience, or from a second display visible only to the assistant oreditor/producer 150.

In an alternative embodiment, computer based speech recognition is usedto recognize commands including key words spoken by presenter 102 andselect events and navigate through the hierarchy based on the recognizedcommands. In this embodiment, the presenter 102 may refer to anidentified event and cause its selection through the use of a voicecommand such as “let's take a look at this record in Florida” whichwould be parsed by the system and cause the selection of a recordtemperature event in Florida.

In some instances is useful for presenter 102 to have feedback fromintelligent broadcast system 100 regarding where they are in thehierarchy and the additional types of information that may be available.In one embodiment, the hierarchical information, as illustrated in FIGS.9A and 9B, is presented to presenter 102 on a separate monitor, notvisible to the audience.

In an alternate embodiment, presenter 102 receives feedback via audibletones communicated to them via an earpiece. In this embodiment, theposition in the hierarchy can be communicated through a series ofaudible tones (e.g. one tone for being at the top of the hierarchy, twotones for being at the next level, three tones for being at the thirdlevel and so on, with one long tone for indicating the bottom of thehierarchy has been reached).

In one embodiment, the intelligent broadcast system 100 can be utilizedto display information related to severe weather and, in particular,convective storms. Convective storms such as thunderstorms are typicallymonitored through the use of Doppler radar. Doppler radar imagestraditionally provide the basis for discussion of convective storms in aweather presentation and provide a simple visual means for recognizingthe location of the storm. When incorporated into the intelligentbroadcast system 100, convective storm monitoring and analysis can beperformed by identifying convective storms found through Doppler radarimages, performing appropriate analyses on both Doppler radar data andother traditional weather related data sources, and presenting thatmaterial as directed by the presenter. Thus, a convective storm analysissystem may be utilized in conjunction with the intelligent broadcastsystem 100 to create convective storm related data which can serve asthe basis for storm related presentation elements.

Referring to FIG. 13, a convective storm analysis system 1300 is shownwhich can be interfaced to intelligent broadcast system 100. Datarelated to convective storm analysis system 1300 is also stored incurrent data system 140. Convective storm analysis system 1300 receivesinput data, including Doppler radar data 1390, satellite data 1392, andterrestrial weather data 1394. Convective storm analysis system 1300identifies convective weather from the Doppler radar 1390 utilizingidentify convective weather use case 1340. Based on the identificationof the convective weather, a description of the convective weather isgenerated in a develop complete storm description use case 1302.Satellite data 1392 can be analyzed using a determine storm toptemperatures use case 1350. Terrestrial weather data 1394 is analyzedusing a determine lightning flash rates/densities use case 1360, adetermine vertical temperature profiles use case 1370, and a calculate4-D thermodynamic fields use case 1380. Based on Doppler radar data1390, satellite data 1392 and terrestrial weather data 1394, convectiveweather presentation elements can be created using a determineconvective weather presentation elements use case 1310. Based on theconvective weather presentation elements, a develop storm prediction usecase 1320 produces a storm prediction, and the develop complete stormdescription use case 1302 provides analyses of the storm beyond what canbe obtained from Doppler radar 1390. A publish and predict weather usecase 1330 is used to generate predictions which are not directly relatedto the severe weather or convective storm.

Convective storm analysis system 1300 provides the ability to extend andrefine radar based storm descriptions by extraction of additional andrelevant information from secondary sources. Exemplary types of stormdescriptors that can be derived using convective storm analysis system1300 include: lightning flash rates and densities obtained throughanalysis of a lightning detection and location dataset; derivation ofstorm top temperatures from infrared satellite observations;storm-relative helicity along the past and future track of the stormusing a 4-D windfield from a numerical weather prediction dataset;estimates of the intensity change potential from 4-D thermodynamicfields from a data assimilation system; and refined hail presenceestimates using vertical temperature profiles from numerical weatherprediction models.

In operation, convective storm analysis system 1300 utilizes data fromDoppler radar 1390 to determine the presence of convective weatherincluding position and movement. Based on the storm position andmovement information as determined from the Doppler radar data,databases of auxiliary meteorological information can be automaticallyanalyzed for relevant complimentary information. When utilized inconjunction with intelligent broadcast system 100, presentation elementscan be formed containing the relevant storm information and madeaccessible to presenter 102.

One example of the application of rules used with the convective stormanalysis system 1300 and the intelligent broadcast system 100 is thefollowing:

-   -   IF Lightning(Within 50 miles of BOS, Moving toward BOS)        -   OR    -   IF Radar(>35 dBZ radar reflectivity within 50 miles of BOS,        Moving toward BOS),        -   THEN            -   FORM_PRESENTATION_ELEMENT(60 minute loop of regional                radar and lightning centered between lightning/35 dBZ                reflectivity and BOS)        -   AND            -   FORM_PRESENTATION_ELEMENT(30 minute loop of forecasted                regional radar centered between lightning/35 dBZ                reflectivity and BOS)        -   AND            -   IF (Convective Storm identified for location of                lightning/35 dBZ reflectivity)        -   THEN            -   FORM_PRESENTATION_ELEMENT(Convective Storm track overlay                for 30 minute loop of forecasted regional radar)            -   AND DISPLAY (When click on lightning/35 dBZ                reflectivity)        -   AND            -   FORM_PRESENTATION_ELEMENT (Effected population table                overlay for 30 minute loop of forecasted regional radar)            -   AND DISPLAY (When click on Convective Storm track                overlay)

In one embodiment, convective storm analysis system 1300 uses stormposition and movement data from Doppler radar 1390 as the basis foranalysis of the Rapid Update Cycle (RUC), a numerical model run by theNational Weather Service approximately every hour and used as the basisfor determining short-term forecasts and small-scale (mesoscale) weatherfeatures. In another embodiment, the Weather Research and Forecasts(WRF) model is analyzed based on the storm position and movement asdetermined from Doppler radar 1390. Both of these databases may beutilized to determine convective storm attributes including but notlimited to: Convective Available Potential Energy (CAPE), ConvectiveINhibition (CIN), lifted index, downdraft CAPE, Lifting CondensationLevel (LCL) height, Level of Free Convection (LFC) height, sheer,storm-relative helicity, storm-relative winds, super cell probability,tornado probability, significant tornado probability, significant severeweather probability, energy-helicity index, vorticity generationpotential, and storm rank along the past and projected future path ofthe storm

Doppler radar data can also be further analyzed based on storm positionand movement and tools such as the Open systems Radar Products Generator(ORPG), produced by the National Severe Storms Laboratory or othercommonly known algorithms can be utilized to obtain additional storminformation including storm mass, volume, storm top divergence,Vertically-Integrated Liquid (VIL) water, probability of hail, andprobable hail characteristics.

When used in conjunction with intelligent broadcast system 100, theconvective storm analysis system 1300 provides presenter 102 withhierarchical information about convective storms and allows presenter at102 to retrieve convective storm related information that cannot beascertained quantitatively from visual inspection of the Doppler radardata alone.

For example, presenter 102 may be describing series of thunderstormslocated in the greater Boston area, those thunderstorms being identifiedon a map by markers of thunderclouds with lightning bolts. Uponselecting a desired marker or icon, a display of the current lightningrate, possibly including a graphic or chart illustrating the currentlightning rate, can be displayed. If the presenter chooses to go intofurther into the hierarchy of presentation elements, additionalinformation related to the thunderstorm can be presented including livevideo of the storm and associated lightning (when available), graphicsillustrating lightning flash rates, hail probability estimates, stormseverity measurements, detailed predicted path of the thunderstorm,impact of the thunderstorm on airport traffic, or other items relevantto the current thunderstorm and of potential interest to the viewer.

The methods and systems described herein with respect to the intelligentbroadcast system 100 may be implemented on a number of computingplatforms and may also be implemented by integrating a computingplatform with one or more general purpose broadcast production systems.

In one embodiment, a computing platform based on a standard personalcomputer running an operating system such as Linux, Windows or UNIX isused to realize intelligent broadcast system 100 and interfaces withcommercially available weather presentation tools such as WeatherProducer and TrueView Interactive, both offered by the WSI Corporationof Andover, Mass.

In an alternate embodiment, intelligent broadcast system 100 isimplemented on a computing platform along with the completefunctionality for weather presentation production. In this embodimentthe tools required for development of the weather presentation are alsoincluded within intelligent broadcast system 100.

In one embodiment, presentation elements are prepared in advance of thepresentation and are created based on the selection of identifiedevents. In this embodiment, the presentation elements are accessed whena marker corresponding to the desired, identified event is selected. Atthat time, the appropriate template is applied to create the finalpresentation materials and presentation elements associated with theevent.

In an alternate embodiment, the presentation elements are not prepareduntil the point at which the marker corresponding to the desired eventis selected. In this embodiment, only presentation elementscorresponding to the identified events are prepared, and presentationelement formation and the application of templates to produce the finalpresentation material do not occur until it is known that thosematerials will be required.

In another embodiment the presentation elements are prepared in advance,based on the existence of marked or identified events. In thisembodiment once events are marked, data representations are formed andtemplates are applied to create final presentation elements andpresentation materials. The final presentation elements are stored andmade ready for access upon selection of the corresponding marked event.

Pre-fetch and branch prediction can also be applied to the presentmethod and system to prepare data representations, apply templates toprepare presentation elements, or for both the creation of presentationelements and the preparation of presentation materials. Branchprediction is utilized by monitoring all possible paths in the hierarchythe presenter may explore based on the selection of a particular event,and by preparing the materials corresponding to those branches at thetime the parent event is selected. Referring to FIG. 9A, if presenter102 chooses current weather conditions of interest 302, branchprediction would be applied to prepare presentation elements related tostorm tracking 304, hazardous conditions 306, weather related news 308,and extremes 310. Even though presenter 102 may not choose any of thoseitems under current weather conditions of interest 302, the materialswill have been automatically prepared and will allow instantaneousdisplay upon selection.

In another embodiment, the presenter 102, user or viewer of theintelligent broadcast system 100 need not be presented with theidentified events and/or select one or more of those events for display.That is, the intelligent broadcast system 100 may automatically (i.e.,through a default condition, for example) display a presentation to thepresenter 102 or user of the system (perhaps of the most relevant orhighest priority event) upon identification of one or more events ofinterest that satisfy the rules. In such a case, the presenter 102 wouldnot have the option of selecting the identified event for whichpresentation elements are displayed. In an alternative embodiment, thepresenter 102 still has the ability to navigate through the sequence orhierarchy of the displayed presentation as well as select additionalevents within the presentation for further information or display. Thoseskilled in the art will recognize that a human presenter is not requiredin order to deliver effective storm presentations to end users sinceautomated display sequences generated using this method are oftensufficient to create display sequences adequate for deliveringcompelling and informative presentations.

Although the method and system described herein has been discussed inthe context of weather and traffic presentations, intelligent broadcastsystem 100 can be utilized for the presentation of a wide variety ofmaterials including economic and business analyses, political news,environmental monitoring, transportation and logistics, militaryoperations, and other presentations that can be organized and presentedin a hierarchical manner.

Referring to FIG. 14, in one embodiment, a weather severity andcharacterization system 700 collects data related to weather phenomenaand other ground and atmospheric conditions at various locationsincluding, among other systems, satellite imagery centers 704 thatreceive data from satellites 705, surface weather observation stations706, lightning detection systems 708, or radar processing stations 714,716. Such weather related data may be transferred over a variety ofpublic and/or private wired and wireless networks 720, including theInternet, to the storm identification and characterization system 740,described in greater detail below. Subsets of these data collectionapparatuses may provide particular data relevant to characterizing ofone or more storms, weather phenomena, weather events or objects 702.

Previously gathered data may be present in weather databases 710, 712.Additional data may also be gathered from vehicles or mobiletransmitters/receivers, including aircraft 792, ships 794 and groundtransportation 796, along with information regarding their locations.Vehicles may transmit, receive, or transmit and receive to and from oneof a system of transmitters and receivers 790. The system may alsocollect some types of data from mobile users 784 using handheld orportable devices 782 via a wireless network 780. Such data may includeone or more of weather-related data, imagery, video, audio, or relatedposition information. Data from each source may be produced in differentformats. In one embodiment, one or more data sources would provideinformation over the network 720 to the storm identification andcharacterization system 740 in an extensible markup language format.

Data are collected from a variety of sources by the storm identificationand characterization system 740. In one embodiment, the weather severityand characterization system 700 processes collected weather data andidentifies particular storms 702 and their characteristics andparameters. In general, characteristics of a storm include descriptionsof the physical or observable properties associated with a storm. Forexample, storm characteristics may include or reflect the degree and/orfrequency of lightning, type of precipitation, amount or severity ofprecipitation, speed or direction of wind, direction and speed of stormmovement, etc. The parameters of a storm generally form a numericalrepresentation of storm related information. The storm identificationand characterization system 740 applies forecasting and other predictiontechniques, including predictive models to predict future values ofparameters of the identified storms. These parameters, also referred toas storm related parameters, may include location, wind velocity, hailsize, lightning flash rate, flood potential, or any other weather orstorm related information, and are discussed in greater detail below.Results of the data analysis may be stored in a storm database 760. Thestorm database 760 may reside on a PC or server, or distributed servers.It could use commercial or open source database platforms such asOracle, Microsoft SQL Server, MySQL, or PostgreSQL. The stormidentification and characterization system 740 may provide externalcommunication through database connections, custom interfaces, or a webapplication server, or any other communications medium or systemgenerally known in the art.

Portions or all of the weather data and/or storm related parameters mayalso be transferred to an Internet storm data server 764. The Internetstorm data server 764 may be a simple PC, a web server, a combination ofseparate web server, application server, and database server, or otherarrangement of server resources. Either a storm data distribution system762 or the Internet storm data server 764 could provide storm dataand/or storm related parameters over the network 720 to other networksystems or to PCs 766 with attached monitors 769 displaying Internetbrowsers operated by users 768. In another embodiment, the Internetstorm data server 764 is accessed by mobile users 784 of portabledevices 782 via a wireless communication network 780.

The Internet storm data server 764 could serve a web page containingboth HTML and JavaScript code. The JavaScript code could periodically,or upon user interaction, obtain additional or more up-to-date weatherdata from the Internet storm data server 764 without reloading the webpage. In one embodiment, the data is in extensible markup language form.

In one embodiment, the storm data distribution server 762 provides stormdatabase updates to subscriber databases at broadcast studios 770 orother locations. The database transfers could be full or incremental andin one embodiment, could be accomplished using the transfer ofextensible markup language (XML) data.

In another embodiment, the weather data and storm related parameters areused at the broadcast studio 770 in the production of weather-relatedprogramming. A presenter 102 may use software and hardware tools tonavigate through graphical and/or textual display of the storm relatedparameters, weather and storm data and other weather related informationsupplied by the storm data distribution system 762 to the remotedatabase 771 of the broadcast studio 770. Alternatively, the broadcaststudio 770 may obtain information directly from the storm datadistribution system 762 itself.

Data may also be provided by either the storm data distribution system762 or the Internet storm data server 764 to a third-party server 774.In one embodiment, the operator of the storm identification andcharacterization system 740 could provide data to third-parties, whowould then provide value-added analysis or repackaging of the data.

In one embodiment, data from the storm data distribution system 762 isused by third-parties to provide value-added services. For example, asearch engine operator may provide recent weather data results inresponse to weather-related keywords. For instance, an Internet searchfor “thunderstorms Raleigh” could produce a near current map of activethunderstorms in the area of Raleigh. Alternately, an Internet searchfor “thunderstorm Raleigh” may launch an automatic presentation similaror identical to a recent on-air presentation related to weather in thatarea. The graphical results could be provided with regions responsive tofurther user input, allowing the user to trigger display of additionalinformation about a selected storm. Similarly, a search for “weathertomorrow” could trigger access to a forecast portion of the Internetstorm data server data 764 to provide forecasted weather parameters forthe user's location. In each case, the search could be conducted on datatransmitted to the search engine provider's database, or via calls tothe Internet storm data server 764 or similar resource provided on thenetwork 720.

In another embodiment, functionality similar to that provided to thebroadcast studio 770 may also be provided to Internet or network users768. Instead of an interface adapted for on-air presentation of weatherdata and forecasts, the information could be presented via a web-basedinterface through an Internet browser or customer application on theusers' PCs 766 to allow interactive exploration of past and forecastedweather and storm data and storm related parameters. Alternatively, theInternet user 768 may be presented with an automated sequence ofdisplays providing the same content as the on-air presentation. A usercould enter the URL of an Internet storm data server 764. The servercould attempt to distinguish the user's location from IP addressinformation, from a previously stored browser cookie, or from userinput. The user could also enter a different location for which hewishes to see weather data. The weather severity and characterizationsystem 700 could then generate a sequence of displays, graphs, images orother presentation material that reflects, for example, the most severestorms in the vicinity of the user's request.

FIG. 16 shows a flow diagram for identifying and characterizing storms,weather events or objects in accordance with the weather severity andcharacterization system 700. Referring to FIGS. 14 and 16, weatherrelated data is received 800 by the storm identification andcharacterization system 740. Weather related data is obtained from avariety of primary and secondary sources. Primary sources include one ormore radar sites 714, 716. Secondary sources include satellites 705,surface weather observation stations 706, lightning detection systems708, and any other weather data observation, measurement or collectionsystem, method or mechanism generally known in the art. Additionally,weather data may be obtained from other secondary sources, such as oneor more third party or independent weather databases 710, 712. Examplesof such weather databases include the output from a Numerical WeatherPrediction system (NWP), a lightning detection and location database, orconsolidated, value-added weather databases such as the MADIS datasetproduced by the National Weather Service, but could be any alternatesource of weather related data that provides information that is used tocharacterize a storm. Weather related data can also be obtained from avariety of mobile devices equipped with transceivers that can sendweather related data and be received by the storm identification andcharacterization system 740. Such mobile devices may include portabledevices 782, airplanes 792, boats 794, and automobiles 796. The weatherrelated data that is received by the storm identification andcharacterization system 740 may be relevant to a selected geographiclocation, region or other area of interest.

In one embodiment of the present invention, weather related data isreceived by the storm identification and characterization system 740.For a region of interest, the weather related data is analyzed todetermine the presence of weather phenomena, storms, or any otheridentifiable weather-related objects. For simplicity, storm is usedherein to denote any type of weather phenomenon, weather event, storm,or other identifiable weather related object, such as cold fronts orwarm fronts. Weather phenomena include any type of storm, includingconvective storms, such as thunderstorms and thunderstorm systems,tornado, hurricanes, winter storms, fronts, and any other type of storm,storm system weather event, or weather system recognized by one skilledin that art. Any procedure for determining and/or identifying thepresence of storms may be used. Each storm in a region of interest isidentified and independently characterized with set of storm relatedparameters for that point in time. At a particular point in time, thestorm exists in a particular state. The state of the storm encompassesthe totality of information to completely describe the characteristicsof the storm at a single point in time. The single point in time may bein the past, present, or future. Thus, a single storm has manyassociated states, including past states, present or current states, andfuture or predicted states. It is also possible that some of theinformation describing one of the states of a storm is unknown. At sometime in the future, new weather related information or data may bereceived by the storm identification and characterization system 740.The presence of storms related to that data is again determined suchthat previous instances of storms are identified and associated with anypreviously determined storm related parameters. Additionally, new stormsare identified for characterization.

Based on the received weather related data, a storm is identified 802 bythe storm identification and characterization system 740. The stormidentification and characterization system determines if the identifiedstorm is an instance of a previous storm or a new storm instance 804. Ifa particular storm has been previously identified, the previouscharacterization data for that storm is retrieved 807, and may be usedwith the presently received weather data by the storm identification andcharacterization system 740 to characterize the storm with a set ofstorm related parameters 808. In doing so, the previously identified orcalculated parameters associated with that storm may be taken intoaccount in generating or predicting the future parameters orcharacteristics of the storm. If a storm is a new instance oridentification 806, then a first set of storm related parameters for thestorm is obtained using the received weather data and future predictedstorm related parameters are determined 808. Once determined, the setsof storm related parameters may be stored 811 and/or used to displaystorm information and parameters in a weather related presentation 812.

In one embodiment of the present invention, a storm is characterized bya set of storm related parameters, with each storm having individualizedvalues for the available or potential storm related parameters. In oneembodiment, the set of storm related parameters used to describe a stormis time dependent. That is, the parameters may have actual values in thepresent and past based on observation, measurement, calculation, orderivation, and may also have predicted future values that areforecasted based on recently measured or computed parameter values orother obtained data. The predicted or forecasted future parameter valuesmay be obtained through one or more various forecasting methods, such asthe application of mathematical models and algorithms. Such models maybe developed, for example, from analysis of historical storm data andparameters.

The storm related parameters of the identified storms are obtained in anumber of ways. They can be observed, measured, calculated, or derived.Some of the storm related parameters are obtained from the physicalweather related data that is directly observed or measured, such asmeteorological data obtained from radar or satellite imagery, surfaceobservations, lightning detection systems, or any other system ormechanism generally known in the art. Some of the storm relatedparameters are calculated using a suite of algorithms that ascertainbasic characteristics of each storm. One group of calculated stormrelated parameters are calculated using data available from the radar.Some examples include location, size (volume, mass), echo top, motion,and the likely existence of an associated tornado. Such computations area common aspect of weather analysis and forecasting systems used todayand are well known to those of ordinary skill in the art. Additionaltypes of calculated storm related parameters are obtained usinginformation from the radar-based calculated storm related parameters andinformation from secondary data sources. Examples of these secondarydata sources include 4D gridded analyses and forecasts of basicmeteorological variables from NWP systems, and geopolitical databases.Examples of the parameters calculated using these data sources includethe likelihood of the storm to contain hail, the lightning flash rate,the convective potential energy, and the total population affected byeach storm. The storm identification and characterization system 740 isthus able to automatically determine storm related parameters for astorm relative to past, present and future time periods based onpresently received weather related data and suite of algorithms. Forexample, a non-exhaustive list of storm related parameters that could bedetermined for an identified storm for a point in time (including futuretimes) is shown in Table 1 below.

TABLE 1 Storm Related Exemplary Typical Parameter Typical Units RangeRange categories Description Storm Severity Index dimensionless N/A 0-10weak 0-3, Index describing the severity of a mod 3-6, storm, based onthe potential for strong 6-10 tornadoes, hail, winds, lightning, andflash flooding Tornado Impact dimensionless N/A 0-10 weak 0-3, Thetornadic potential of the storm mod 3-6, based on some of the otherstorm strong 6-10 related parameters Hail Impact dimensionless N/A 0-10weak 0-3, The hail potential of the storm mod 3-6, based on some of theother storm strong 6-10 related parameters Wind Impact dimensionless N/A0-10 weak 0-3, The damaging wind potential of mod 3-6, the storm basedon some of the strong 6-10 other storm related parameters LightningImpact dimensionless N/A 0-10 weak 0-3, The potential fordeadly/damaging mod 3-6, lighting in the storm based on strong 6-10 someof the other storm related parameters Flooding Impact dimensionless N/A0-10 weak 0-3, The flash flooding potential of the mod 3-6, storm basedon some of the other strong 6-10 storm related parameters Sfc-based CAPEJ/kg 0 to infinity 500-5000 weak 0-1000, A measure of generalatmospheric mod 1000-2500, instability strong 2500-5000 Mixed-layer CAPEJ/kg 0 to infinity 500-5000 weak 0-1000, Measure of general atmosphericmod 1000-2500, instability strong 2500-5000 Most unstable CAPE J/kg 0 toinfinity 500-5000 weak 0-1000, Measure of general atmospheric mod1000-2500, instability strong 2500-5000 Sfc-based CIN J/kg 0 to infinity 0-500 weak 0-50, Measure of atmospheric capping, moderate 50-250, orresistance to thunderstorm strong initiation; larger values mean 250-500thunderstorm development is less likely Mixed-layer CIN J/kg 0 toinfinity  0-500 weak 0-50, Measure of atmospheric capping, moderate50-250, or resistance to thunderstorm strong initiation; larger valuesmean 250-500 thunderstorm development is less likely Most unstable CINJ/kg 0 to infinity  0-500 weak 0-50, Measure of atmospheric capping,moderate 50-250, or resistance to thunderstorm strong initiation; largervalues mean 250-500 thunderstorm development is less likely Sfc-basedLifted Index degrees C. negative −15 to 0   weak 0 to −3, Measure ofmiddle atmospheric infinity to moderate −3 instability; more negativevalues infinity to −6, strong −6 usually mean more intense to −15thunderstorms Mixed-layer Lifted Index degrees C. negative −15 to 0  weak 0 to −3, Measure of middle atmospheric infinity to moderate −3instability; more negative values infinity to −6, strong −6 usually meanmore intense to −15 thunderstorms Most unstable Lifted degrees C.negative −15 to 0   weak 0 to −3, Measure of middle atmospheric Indexinfinity to moderate −3 instability; more negative values infinity to−6, strong −6 usually mean more intense to −15 thunderstorms DowndraftCAPE J/kg 0 to infinity  0-1000 weak 0 to Measure of potential energy of100, thunderstorm downdrafts; can moderate give some indication of how100-500, strong thunderstorm winds will be strong 500-1000 Sfc-based LCLmb 0 to infinity 700-1000 low 900 to Surface Based Lifting 1000, modCondensation Level; the level at 800-900, high which a surface-based airparcel 700-800 will condense when lifted Mixed-layer LCL mb 0 toinfinity 700-1000 low 900 to Mixed-Layer Lifting Condensation 1000, modLevel; the level at which an air 800-900, high parcel representing theaverage 700-800 conditions in the lowest 100 mb will condense whenlifted Most unstable LCL mb 0 to infinity 700-1000 low 900 to MostUnstable Lifting 1000, mod Condensation Level; the level at 800-900,high which an air parcel based at the 700-800 most unstable point in thevertical column will condense when lifted Sfc-based LFC mb 0 to infinity500-1000 low 850 to Surface Based Level of Free 1000, Convection; thelevel at which a moderate 700 condensed, surface-based air to 850, highparcel will freely rise due to 500 to 700 atmospheric instabilityMixed-layer LFC mb 0 to infinity 500-1000 low 850 to Mixed-Layer Levelof Free 1000, Convection; the level at which a moderate 700 condensed,mixed-layer air parcel to 850, high will freely rise due to atmospheric500 to 700 instability Most unstable LFC mb 0 to infinity 500-1000 low850 to Most Unstable Level of Free 1000, Convection; the level at whicha moderate 700 condensed air parcel based at the to 850, high mostunstable point will freely rise 500 to 700 due to atmosphericinstability BRN shear meters²/second² 0 to infinity 10-50  weak 0 to 15,Bulk Richardson Number shear mod 15 to 30, strong 30 to 50 0-1 km shearmeters/second 0 to infinity 5-25 weak 0 to 5, Vertical wind shear in thelowest 1 km mod 5 to 15, of the atmosphere; higher strong 15 to valuesare associated with more 25 organized thunderstorms and higher tornadopotential 0-6 km shear meters/second 0 to infinity 5-50 weak 0 to 10,Vertical wind shear in the lowest 6 km mod 10-30, of the atmosphere;higher strong 30-50 values are associated with more organizedthunderstorms 0-1 km stm relative meters²/second² 0 to infinity  0-600weak 0-75, Storm-relative helicity in the helicity mod 75-250, lowest 1km of the atmosphere; strong 250-600 higher values are associated withhigher tornado potential 0-3 km stm relative meters²/second² 0 toinfinity  0-600 weak 0-75, Storm-relative helicity in the helicity mod75-250, lowest 3 km of the atmosphere; strong 250-600 higher values areassociated with higher tornado potential and longer-live thunderstormsHeight of Wet-Bulb meters 0 to infinity 1500-4000  low 1500-2000,Freezing level of wet-bulb Zero mod temperature, an indication of hail2000-3000, size/potential high 3000-4000 CAPE in the −20 to −40 J/kg 0to infinity  0-1000 weak 0-250, Atmospheric instability in the levellayer mod 250-500, of the atmosphere where the strong 500-1000temperature is between −20 and −40 C.; related to hail size/f FreezingLevel Height meters 0 to infinity 2000-5000  low 2000-3000, Height ofthe freezing level; mod related to hail occurrence/size 3000-4000, high4000-5000 Height of −20 C. meters 0 to infinity 5000-10000 low5000-6000, Height of the −20 level; related to mod hail occurrence/size6000-7500, high 7500-10000 Supercell Index dimensionless 0 to infinity0-25 weak 0-5, Composite index describing the mod 5-10, favorability forsupercell strong 10-25 development Significant Tornado dimensionless 0to infinity 0-10 weak 0-1, Composite index describing the Index mod 1-3,favorability for occurrence of strong 3-10 significant tornadoesSignificant Severe Wx dimensionless 0 to infinity  0-100 weak 0-10,Composite index describing the Index mod 10-30, favorability foroccurrence of strong 30-100 significant severe weather (large hail,damaging winds, tornadoes) Significant Hail Index dimensionless 0 toinfinity 0-5  weak 0-1, Composite index describing the mod 1-2,favorability for occurrence of large strong 2-5 hail Energy-HelicityIndex dimensionless 0 to infinity 0-6  weak 0-1, Index describing thefavorability mod 1-2.5, for supercells/tornadoes; based strong 2.5-6 ona combination of atmospheric instability and wind shear Showalter Indexdegrees C. negative −12 to 3   weak 3 to 0, Measure of middleatmospheric infinity to moderate −0 instability; more negative valuesinfinity to −6, strong −6 usually mean more intense to −12thunderstorms; more useful than Lifted Index if storms are elevated KIndex degrees C. negative 10 to 50  weak 10-20, Index used to predictair-mass infinity to moderate 20-35, thunderstorm development infinitystrong 35-50 VIL kg/meters² 0 to infinity 10-100 weak 0-10, VerticallyIntegrated Liquid in a mod 10-30, storm, associated with general strong30-100 storm severity and hail size VIL Density grams/meters³ 0 toinfinity 0-10 weak 0-2, Vertically Integrated Liquid in a mod 2-5, stormdivided by storm height, strong 5-10 associated with general stormseverity and hail size Height of Center of meters 0 to infinity2000-10000 low 2000-4000, Height of center of storm mass, Mass modtypically associated with storm 4000-6000, intensity and hailproduction/size high 6000-10000 Height of Max dbZ meters 0 to infinity2000-10000 low 2000-4000, Height of largest reflectivity value, modtypically associated with storm 4000-6000, intensity and hailproduction/size high 6000-10000 Height of 50 dbZ meters 0 to infinity2000-10000 low 2000-4000, Height of 50 dBZ value, typically modassociated with storm intensity 4000-6000, and hail production/size high6000-10000 Height of 50 dbZ above meters negative  0-7000 low 0-1000,Height of 50 dBZ value above the 0 isotherm infinity to mod 1000-3000,freezing level, typically associated infinity high with storm intensityand hail 3000-7000 production/size Height of 50 dbZ above metersnegative −2000 to 5000  low −2000 to −1000, Height of 50 dBZ value abovethe −20 isotherm infinity to mod −1000 −20 C level, typically associatedinfinity to 1000, with storm intensity and hail high 1000-5000production/size Reflectivity at 0 dBZ negative 20-70  weak 20-40,Reflectivity at the freezing level, isotherm infinity to mod 40-50,typically associated with storm infinity strong 50-70 intensity and hailproduction/size Reflectivity at −20 dBZ negative 20-70  weak 20-30,Reflectivity at the −20 C level, isotherm infinity to mod 30-40,typically associated with storm infinity strong 40-70 intensity and hailproduction/size Max dbZ dBZ negative 40-70  weak 20-40, Highestreflectivity in storm, infinity to mod 40-50, general indication ofstorm infinity strong 50-70 intensity Max Hail Size inches 0- 0-4  small0-0.5, Maximum expected hail size unlimited medium 0.5-1.5, large 1.5-4Prob of Severe Hail percentage 0-100  0-100 weak 0-20, Probability ofsevere hail (>0.75″) mod 20-60, strong 60-100 Prob of Hail percentage0-100  0-100 weak 0-20, Probability of hail mod 20-60, strong 60-100Precip Rate mm/hr 0 to infinity  0-100 light 0-10, Max precipitationrate in a storm mod 10-30, heavy 30-60 Storm Top km 0 to infinity 5-20low 5-10, Highest instance of an 18 dBZ mod 10-15, echo high 15-20Deviant Storm Motion degrees −180 to −45 to 90  weak −45 to Rightwarddeviation of storm in 180 0, mod 0-20, degrees relative to mean stormstrong 20-90 motion; indicative of storm rotation and intensity DeviantStorm Speed percentage 0 to infinity 25 to 200 weak 120-200, Ratio ofstorm speed to mean mod 90-120, storm speed; a storm that slows strongdown is usually intensifying 25 to 90 Storm Age minutes 0 to infinity  0to 240 young 0-20, Age of storm mature 20-100, old 100-240 Storm Masskg * 10⁸ 0 to infinity   0-15000 small 0-3000, Storm water mass,effectively VIL mod 3000-9000, multiplied by storm footprint large9000-15000 Storm Volume km³ 0 to infinity  0-1500 small 0-250, Stormvolume (size) mod 250-750, large 750-1500 Storm Footprint km² 0 toinfinity  0-500 small 0-100, Projection of the storm on the mod 100-200,Earth's surface large 200-500 Maximum Radial m/s 0 to infinity 0-75 weak0-15, Maximum detected wind speed in Velocity mod 15-30, storm strong30-75 Cyclonic Shear Mass /100 s 0 to infinity 0-20 weak 0-5, Cyclonicshear integrated over the mod 5-10, entire storm strong 10-20 CyclonicShear Mass /100 s * 1000 km³ 0 to infinity 0-10 weak 0-3, Integratedcyclonic shear divided Density mod 3-6, by storm volume strong 6-10Anticyclonic Shear /100 s 0 to infinity 0-10 weak 0-3, Anticyclonicshear integrated over Mass mod 3-6, the entire storm strong 6-10Anticyclonic Shear /100 s * 1000 km³ 0 to infinity 0-5  weak 0-1,Integrated anticyclonic shear Mass Density mod 1-3, divided by stormvolume strong 3-5 Total Shear Mass /100 s 0 to infinity 0-30 weak 0-10,Total (Anticyclonic + Cyclonic) mod 10-20, shear integrated over theentire strong 20-30 storm Total Shear Mass /100 s * 1000 km³ 0 toinfinity 0-10 weak 0-3, Integrated total shear divided by Density mod3-6, storm volume strong 6-10 Meso Base km 0 to infinity 0-10 strong0-2, Height of mesocyclone base - moderate 2-4, lower bases are moredangerous weak 4-10 Meso Depth km 0 to infinity 0-10 shallow 0-2, Depthof mesocyclone moderate 2-4, deep 4-10 Meso Low-Level m/s 0 to infinity0-50 weak 0-10, Rotational velocity at lowest tilt of RotationalVelocity moderate 10-25, mesocyclone strong 25-50 Meso Max Rotationalm/s 0 to infinity 0-50 weak 0-10, Max rotational velocity of Velocitymoderate 10-25, mesocyclone strong 25-50 Meso Max Shear /s 0 to infinity 0-0.1 weak 0-0.02, Max shear of mesocyclone moderate 0.02-0.05, strong0.05-0.1 Meso Max gate-to-gate /s 0 to infinity  0-0.1 weak 0-0.02, Maxgate-to-gate shear of shear moderate mesocyclone 0.02-0.05, strong0.05-0.1 Meso Low-Level km 0 to infinity 0-20 small 0-5, Low-leveldiameter (perpendicular Diameter moderate 5-15, to radar beam) ofmesocyclone large 15-20 TVS Base km 0 to infinity 0-10 strong 0-2,Height of TVS base - lower bases moderate 2-4, are more dangerous weak4-10 TVS Depth km 0 to infinity 0-10 shallow 0-2, Depth of TVS basemoderate 2-4, deep 4-10 TVS Low-Level gate-to-gate /s 0 to infinity 0-0.1 weak 0-0.01, Gate-to-gate shear at lowest tilt of shear moderateTVS detection 0.01-0.03, strong 0.03-0.1 TVS Max gate-to-gate shear /s 0to infinity  0-0.1 weak 0-0.01, Max gate-to-gate shear of TVS moderate0.01-0.03, strong 0.03-0.1 CG Lightning Density /min * 100 km² 0 toinfinity 0-10 weak 0-2, Total lightning strikes per storm moderate 2-4,per minute per 100 km{circumflex over ( )}2 strong 4-10 Total CGLightning /min 0 to infinity 0-10 weak 0-2, Total lightning strikes perstorm Strikes moderate 2-4, per minute strong 4-10 Positive CG Lightningpercentage 0-100  0-100 weak 0-20, Ratio of positive to negative Ratiomoderate 20-40, lightning strikes in the last minute strong 40-100 TotalCG Positive kA/min 0 to infinity 0-10 weak 0-2, Sum of current from allpositive Current moderate 2-4, lightning strikes per minute in thestrong 4-10 storm Positive CG Current Density kA/min * 100 km² 0 toinfinity  0-200 weak 0-40, Sum of current from all positive moderate40-80, lightning strikes per minute in the strong 80-200 storm per 100km{circumflex over ( )}2 Total CG Negative kA/min 0 to infinity 0-10weak 0-2, Sum of current from all negative Current moderate 2-4,lightning strikes per minute in the strong 4-10 storm Negative CGCurrent kA/min * 100 km² 0 to infinity  0-200 weak 0-40, Sum of currentfrom all negative Density moderate 40-80, lightning strikes per minutein the strong 80-200 storm per 100 km{circumflex over ( )}2

As shown in the first six rows of Table 1, some of the storm relatedparameters calculated by the storm identification and characterizationsystem 740 are an impact number, or severity index, that has beenderived from the other storm related parameters and which are relevantto a particular types of storm features or characteristics. The termsimpact number and severity index may be used interchangeably to describeembodiments of the present invention. Each impact number is asimple-to-understand metric that represents the relative or normalizedlevel of a feature, characteristic or threat imposed by that storm orweather phenomenon. These severity indices include a flood threat impactindex, a lightning threat impact index, a hail threat impact index, atornado threat impact index, and wind threat impact index, and arederived using information from a combination of the of the observed,measured, and calculated and parameters described above. Those skilledin the art will recognize that other or additional types of indices maybe utilized to represent other types of characteristics or features ofweather phenomena, depending on the desired characteristics to beviewed. An additional index is a composite severity index which is anaggregate or composite of one or more of the individual indices for aweather phenomenon. Examples of metrics that can be used to representthe impact numbers or severity indices are a range of numbers (e.g.,1-10, or 0-5), a set of well-known or human language orienteddescriptive phrases (e.g., pea, golf ball, baseball, for example, toconvey a relative size of hail), color coded bars, varying length bars,a set of symbols, or even some combination of the preceding examples. Auseful feature of these severity indices is that they are a normalizedscale that allows for a quick comparison of the potential impact topopulation and property for separate weather phenomena. Each of theseverity indices is an aggregated representation of the observed,measured, and derived information about a storm contained in at leasttwo of the storm related parameters, which one skilled in the art willrecognize as an improvement to other well known meteorological indicesconveying the damage potential of weather phenomenon, such as theSaffir-Simpson scale for categorizing hurricanes and the Fujita andEnhanced Futija Scales for categorizing tornadoes. As another example,one or more of the storm related parameters or severity indices may beused in conjunction with a display of traffic data or trafficinformation, including display on a roadmap and/or with other geographicfeatures.

FIG. 17 shows an example of the movement of two storms 822, 824 in aregion of interest 820. The position of these storms is changing in timeas represented by the series of circles and arrows for storm 822 andstorm 824. The increments of time are arbitrary (typically minutes todays) and are determined, for example, based on characteristics of thestorms and the observation data from which storm detections are made. Inone embodiment, as shown in FIG. 17, the exemplary time incrementsbetween the storm positions are in minutes, such that the present timeis represented by t=0 826, with t=−10 828 indicating a time ten minutesin the past, and t=10 827 indicating a time ten minutes in the future.At any point in time, there exists a set of storm related parameters,described above, that are used to characterize a storm. For the presenttime, the storm related parameters are actual storm related parametersobtained through a number of means including observation, measurement,calculation, or derivation as described above. Similarly, such stormrelated parameters may be determined for times in the past, where actualstorm related parameters exist for a storm at intervals extending backto the time the instance of that storm was first detected. For times inthe future where t>0, sets of storm related parameters also exist, witha distinction being that they are predicted or forecasted based on avariety of factors, including the actual storm related parameters forthat storm in the present and in the past.

For example, when a storm is first detected, a first set of stormrelated parameters for t=0 is obtained as previously described.Associated sets of storm related parameters for t=5, t=10, etc., arepredicted based upon the initial storm related parameters. If newweather-related data is obtained at a time that is five minutes laterfor the same storm by the storm identification and characterizationsystem 740, a second set of actual storm related parameters is obtaineddescribing the present characteristics of the storm. This second set ofstorm related parameters is now associated with the present time (t=0),and the initial set of actual storm related parameters is now labeledt=−5. It is the same set of storm related parameters that was initiallyobtained for the first detection of the storm, but this set ofparameters is now associated with a time in the past. The predictedfuture storm related parameters associated with t=5 at the initial stormprediction, are stored in an archive and then replaced with actual stormrelated parameters determined by the storm identification andcharacterization system for the present time. New sets of predictedstorm related parameters are determined for time intervals into thefuture. This process can continue for as long as the storm is detectedor identified. If, after a second interval of 5 minutes, a third set ofstorm related parameters is obtained for the new t=0, then the initialset of storm related parameters is associated with t=−10 and the secondset of storm related parameters is associated with t=−5, the current setof storm related parameters with t=0, and new storm related parametersare predicted for the future. In this way, the set of storm relatedparameters with the association of t=0 describes the presentcharacteristics of the storm. For each successive iteration of thisprocess of determining the storm related parameters, the past parametersare retained, and predicted parameters are updated based on appropriateforecasting or predictive methods that may now account for the mostcurrently available weather related data for that storm.

In one embodiment of the invention, the sets of storm related parametersfor the present, and past and future are used to display weatherinformation, and specifically weather information related to aparticular storm. For example, one or more storms in a region ofinterest can be located in the present, tracked in the past, andprojected into the future as well as presented with any combination ofpast, current and predicted storm related parameters associated withthat storm that would provide valuable information to a viewer.Presentation of storm related parameters is described in greater detailbelow.

In one embodiment of the present invention, the storm related parametersare stored in a multidimensional storm database 760. FIG. 18 shows athree dimensional data table 830 representing of a portion of the stormdatabase 760. Storm instances are on a first axis 832. Storm relatedparameters for each storm instance are on a second axis 834. Timerelative to the present time of the storm is on a third axis 836. Thus,for any storm instance, the sets of storm related parameters for past838, present 837 and future 839 are contained in the table. In thisexemplary table, one hundred storm related parameters are shown for afirst storm instance at five time intervals. As new storm instances aredetected they are added to the table 830. It is therefore possible thatall the fields in the table for any given time are not populated. Forexample, the newest storm instances will have no storm relatedparameters for the past time values since such parameters may bedetermined based on actual observation, measurement, calculation orderivation only once the storm has been detected. The data table 830could be considered to be a snapshot of all the actual past, actualpresent, and predicted future storm related parameters as they exist forall the storm instances in the table at a single point in time.

The storm database 760 is created as shown in FIG. 19 from a pluralityof the data tables 830, such that the storm database is 5-dimensional.Each data table 830 contains three dimensions showing storm instance,storm related parameters, and storm relative time as described above. Afourth dimension of absolute time is added along the horizontal axis844. Those skilled in the art will recognize that the absolute time axis844 is not the same as the storm relative time axis 836 in the datatable 830 of FIG. 18. That is, in a data table 830, time is relative tothe present time of the storms in only that particular table. Thus, eachtable 830 will have a different present time (t=0) relative to theabsolute time measured on the axis 844 in FIG. 19. This dimension allowsprevious versions of tables 830 to be stored in order to preserve thepreviously predicted future storm related parameters and to comparethose predicted values with actual values or predicted values previouslyor subsequently calculated. The differences in the predicted and actualvalues of the parameters might be used, for example, to refine thepredictive models used to make predictions about the storm or could beused for other data analysis purposes generally known in the art. Theslice of data 840 represents all the storm related parameters, past,present and future, for a single storm instance at a particular absolutetime. The data slice 860 (at an absolute time in the future) representsall the storm related parameters for the same storm instance utilizingrevised predicted storm parameters.

A fifth dimension of the storm database represents specific elements ofthe storm detection dataset and may represent specific radars,individual satellites and/or other suitable source data instancessuitable for detecting storms. In one embodiment, the fifth dimension ofthe storm database 760 is shown in FIG. 19 on the vertical axis 846 asstorm observation platform instance. Each table 830 in the stormdatabase 760 represents a snapshot at an absolute time of all the stormrelated parameters for storms identified within the effective vicinityof or as obtained from data from a single observation platform. In oneembodiment of the invention, each table 830 is built from the past,present, and predicted future set of storm related parameters for thenumber of detected storms within the vicinity of a single radar site.Since the effective vicinity of adjacent radars can overlap, it ispossible that a single storm can be detected by multiple radars as thatstorm moves from the effective vicinity of one radar site into theeffective area(s) of an adjacent radar site. FIG. 20 shows a storm 850inside the effective vicinity 856 of radar 852 and also within theeffective vicinity 858 of radar 854. Independent recognition of thestorm 850 by both radars 852, 854 may result in the creation of a firstmultidimensional table 832 and a second multidimensional table 835 wheredifferent storm instance numbers may be generated for the same storm ineach table. For example, in table 832, storm 850 is storm instance 1,but in table 835 the same storm 850 is storm instance 5.

In an alternate embodiment of the invention, the dimensionality of thestorm database 760 is reduced to four by removing the storm observationplatform instance axis. All storms tracked by all observation platformsare be combined into a single table 830, with recognition andreconciliation of duplicate storm instances detected by more than oneobservation platform. As an example, where a single storm is identifiedby two separate radar sites 852, 854, such as shown in FIG. 20, thestorm identification and characterization system 740 detects that storminstance 1 from radar 852 is the same storm 850 as storm instance 5 fromradar 854. Accordingly only one set of storm related parameters forstorm 850 is included in a combined observation platform table 830 instorm database 760.

In one embodiment of the invention, the storm related parameters aretransmitted by the storm data distribution system 762 directly from thestorm identification and characterization system 740 to a remote sitefor display or storage. All or some combination of the past, present,and future storm related parameters for a storm at a time of interestare bundled and transmitted. Update information about a storm is bundledand transmitted as new information is provided by the stormidentification and characterization system 740. Updated informationabout a different storm is bundled and transmitted separately. In thisway each bundle of information only contains past present and futurestorm related data about a single storm at one point in time. In analternative embodiment of the present invention, the storm relatedparameters can be retrieved from the storm database 760, bundled, andtransmitted. Referring to FIG. 19, the region 840 contains all the past,present, and predicted storm related parameters for a storm at a pointin time. This data can be retrieved from the database, bundled, andtransmitted to a remote site. Alternatively, the data for each storm ina table 830 can be independently retrieved and bundled. These bundlescan be sequentially assembled into a composite bundle for transmission.

Referring to FIGS. 14 and 21-30, the weather related data and stormrelated parameters, including the various storm impact indices generatedby the weather severity and characterization system 700 for anydefinable or identifiable storm, weather event or object are availablefor display to an operator, presenter 102, consumer or viewer. Aspreviously described, the person or entity to which display of theweather severity and characterization parameters are made available maybe a local or network broadcaster, reporter or meteorologist, weathersupplier, program viewer (e.g., broadcast, cable or satellitetelevision), Internet or other network user, computer system or networkor any combination thereof.

In one embodiment, the display of the storm related parameters describedabove with reference to FIGS. 14-20 is organized in such a manner so asto allow a meteorologist, reporter, presenter 102, or automated displaypresentation system during a live video production to describe thecharacteristics of, for example, convective storms (e.g., thunderstorms)in a broad and temporal manner, such that the display of the weatherseverity parameters account for past, current and predicted (i.e.,future) aspects of the storm or weather event. That is, since thedisplayed storm related parameters and weather data incorporate (in someinstances with some degree of specificity) characteristics of the stormover time (including the future) as well as future positionalinformation, the display of such storm related parameters (including theimpact indices) permits the display of future, predicted or forecastedweather severity characteristics with respect to the current orpredicted location for each particular identified storm or weather eventand how those parameters (and thus the storm) will change over thecourse of the predicted positions at the future times (or vise versa).

Additionally, the presenter 102 or viewer has the ability to control ornavigate the presentation of the weather severity parameters in anintelligent manner similar to that described above with reference tosequenced and/or hierarchal presentations of the intelligent broadcastsystem 100 (see, for example FIGS. 9A-9D). Stated differently, thedisplay or presentation of storm related parameters in the weatherseverity and characterization system 700 may include the use andapplication of user-defined data monitoring and/ornavigation/presentation rules to establish, for example, an automatic orprompted sequence of storm related parameters for a particular weatherevent or group of events, described in greater detail below.

Alternatively, the weather severity and characterization system 700 mayemploy pre-defined data monitoring and/or navigation/presentation rules,such that the presenter 102 or viewer has limited control over the typeand manner of the sequencing available in presenting the severityparameters. That is, the weather severity and characterization system700 may include an implicit display of storm related parameters based onthe identified weather, storms or events. For example, uponidentification of a particular class of weather data (e.g., athunderstorm), the presentation aspect of the weather severity andcharacterization system 700 will display a set number of aspects aboutthat thunderstorm in a set manner should the presenter or viewer selectthat thunderstorm.

In one embodiment of the weather severity and characterization system700, weather data related to a geographic region of interest isobtained. Referring to FIG. 21, for example, the display 900 includesradar data 904 that is obtained for a particular radar site (not shown)relevant to a desired geographic region 902. Alternatively, other typesof weather data generally known to those skilled in the art may beobtained.

The relevant weather data may be obtained from any weather data sourceor combination of sources, including as described above with referenceto FIGS. 14-20. Alternatively, the weather data may be obtained from thestorm related parameters and/or the storm database 760 described above.

In one embodiment, the presentation aspect of the weather severity andcharacterization system 700 displays one or more representations ofweather events, objects or storms that have been identified from orassociated with the weather data in the geographic region 902. Thus, forexample, if the relevant weather data and/or storm related parametershave identified thunderstorms within the region 902, for example,through the storm identification and characterization system 740 and/orstorm database 760 described above, the identified thunderstorms may bedisplayed to the presenter 102 or viewer on a display device 104 such asa computer of video monitor, such that the presenter or viewer is ableto more clearly determine or visualize the presence and/or location ofsuch thunderstorms than if the presenter had been looking only at theunaided weather data (e.g., radar data 904) in the geographic region 902without additional conditioning or markings. Stated differently, thepast, current and/or predicted storm related parameters orcharacteristics of a particular storm may be graphically displayed tothe presenter 102 or viewer using some display scheme or mechanism.

Referring to FIG. 22, in one embodiment, the identified weather eventsor storms are identified or marked on the display 900 for the presenterusing icons 906. In the example of FIG. 22, at least some of thethunderstorms visually identifiable to those skilled in the art based onthe radar data in FIG. 21 have been specifically identified or markedwith icons 906. The icons employed by the presentation aspect of theweather severity and characterization system 700 need not bespecifically related to or associated with commonly used weathersymbols. Rather, the icons may be any marking or symbol that would berecognizable by the presenter or viewer as identifying a storm or anaspect of that storm. The icons need not be any particular shape, coloror size and the use of visible icons of other storm markers is not anecessary aspect for subsequent aspects of the presentation systemdescribed below.

In an alternative embodiment, different types of icons may be used todenote different features and/or storms. In the example of FIG. 22, acircle icon indicates the presence (and location) of a storm generally,a diamond icon indicates that hail is present in or around a storm (seeFIG. 27), a square (see FIGS. 28, 31-33) indicates the presence of amesocyclone (e.g., a rotating storm), and a triangle indicates that atornado is likely in or around that storm (see FIG. 34). In short, thetype of icon or a variation of an icon may be used to represent aparticular historical, existing or predicted state and/or intensity of astorm. For example, referring to FIGS. 33 and 34, which shows a display900 for a storm represented by icon 906 at two different times 990, thesquare icon 906 in FIG. 33 indicating the presence of a mesocyclone hasbeen replaced by a triangular icon 906 in FIG. 34 indicating thepresence of tornadic activity in or around the storm. In one embodiment,the weather severity and characterization system 700 automaticallyplaces the icons 906 on the display 900 according to the relevantlocation of the storm and automatically selects the icon type andvariety according to the storm related parameters and impact indices ofthe storm. As shown in FIG. 22, icons 906 relevant to an identifiedstorm may be layered on top of one another if multiple icons could beused to designate the same storm.

In one embodiment, the icons may be animated, blinking, change colorand/or change shape to denote features or storm related parameters ofone or more of the identified storms.

In another embodiment, the presentation aspect of the weather severityand characterization system 700 employs numbers and/or letters to markthe identified storms. Those skilled in the art will recognize that anysimilar marking mechanism may be used to denote identified storms withinthe region of interest.

In another embodiment, no icons or markings are placed on the display900, such that the presenter 102 visually recognizes the identifiedstorms through, for example, displayed radar data. In such an instance,the presenter 102 may rely on memory or skill to identify the storm ofinterest to obtain further information about the storm.

In one embodiment, the presenter is able to select one of the identifiedstorms (i.e., a storm that the presenter has an interest in) to obtainfurther information (e.g., storm related parameters) about the storm.Such selection may be accomplished by clicking on or touching one of theicons, making a gesture at the display screen 900 or another sensorassociated with the display screen or speaking audible predefinedterminology or natural language voice commands. Other methods ofinitiating an on-screen selection generally known to those skilled inthe art may be used without departing from the spirit and scope of theweather severity and characterization system 700. Identified storms maybe selected from the display 900 or other presentation device 104irrespective of the type of icon or identifying mark (or absence of suchmark) being used.

Upon selection of an icon or identified storm, the presentation aspectof the weather severity and characterization system 700 displays stormrelated parameters related to the selected storm or event. The displayof the storm related parameters(s) may be a one or a combination of avariety of different formats. The presentation aspect of the weatherseverity and characterization system 700 displays the storm relatedparameters for identified weather events or storms in any known tabular,graphical, text, video, audio format(s), any other format generallyknown to those skilled in the art, or any combination thereof. Forexample, referring to FIG. 23, the thunderstorm denoted by the icon 910has been selected. A graphical text box 912 is displayed, and includesthe storm related parameters, and in particular impact indices, of windimpact 920, tornado impact 922, lightning impact 924, flooding impact926 and the composite severity index 930. In the graphical text box 912,the rating for each of these indices is shown in numerical format (e.g.,the composite severity index 930 is 6 and labeled as “Storm SeverityIndex” as chosen by the system operator). The storm related parametersor indices displayed corresponding to the selected storm may include anycombination of past, present and/or predicted future values of thatparameter.

Alternatively or in addition, one or more of the storm relatedparameters and/or impact indices are shown on the display 900 in theform of a line graph (see FIG. 24) or bar graph (not shown). Referringto FIG. 24, the composite severity index 930 for the selected stormcorresponding to icon 910 is shown in a line graph 914 on the display900. Furthermore, the line graph 914 includes the predicted future valueof the composite severity index 930. That is, in the example of FIG. 24,if the current time is 2:05 PM, the line graph 914 includes the pasthalf hour of composite severity index 930, the current value(approximately 6) and the predicted values of the composite severityindex 930 for the next one hour for the selected storm 910. Similarly,in FIG. 25, again assuming that the current time is 2:05 PM, the display900 includes a line graph 916 showing past, current and predicted futurevalues of the hail impact index 928 over time for the selected storm.

In one embodiment, the display of storm related parameters related tothe selected storm includes a historical and/or predicted future path ortrack of the selected storm. Similar to the use of icons 906 to displaythe presence or location of an identified storm, the historical orpredicted future paths may be shown on the display 900 using one or acombination of symbols, icons or other markings, and may includedifferent shapes, sizes and variations thereof. For example, as shown inFIG. 26, the display 900 includes the graphical text box 912corresponding to the selected storm, but also shows the predicted path908 for each of the storms identified with icon(s) 906. FIG. 27 shows adisplay 900 of multiple icons 906 denoting identified storms and theirhistorical paths 909. Similarly, FIG. 28 shows a display 900 thatincludes icons 906 corresponding to multiple identified storms, thehistorical paths 909 as well as their predicted future paths 908. Thedisplay 900 in FIG. 28 further includes a graphical text box 912indicating impact indices for the selected storm.

In one embodiment, upon selection of a storm or icon 906, in addition todisplaying storm related parameters or severity or impact information,the weather severity and characterization system 700 also provides anenlarged (i.e., localized zoom) view of the selected storm. For example,referring to FIG. 29, the display 900 includes weather data (e.g.,zoomed-in radar), the composite severity parameter 930 in the form of aline graph 914 and the historical and predicted paths 909, 908 for theselected storm. FIG. 31 shows a display 900 with an alternate graphicalrepresentation for projected path 908, along with a line graph 914 forthe composite severity index 930. Similarly, FIG. 32 shows a displaywith the alternate graphical representation for the projected path 908,and a graphical text box 912 with the six impact indices. The graphicaltext box 912 also includes the position 992 and velocity 994 data forthe storm.

Referring generally to FIGS. 35 and 37, the parameters displayed to theviewer in the localized zoom view may be changed depending on theseverity of the storm or the specific threat associated with the storm.For example, for a storm where the threat of damaging hail is forecast,the viewer may be interested in knowing the maximum hail size. Theparameter can be displayed along with the impact indices as shown inFIG. 35. In FIG. 35, the display 900 for a relatively strong stormrepresented by icon 906 and projected path 908 includes a graphical textbox 912 that includes the storm severity index 930, the tornado impact922, the hail impact 928, and the maximum hail size 984. In FIG. 37, fora much weaker storm, only the storm severity index 930, the tornadoimpact 922, the hail impact 928 are listed.

In another embodiment, upon selection of a storm or icon 906, the icons906 or markings are animated, such that the icon 906 moves along thehistorical and/or predicted future paths 909, 908 of the selected storm.

In another embodiment, the icon 906 for a particular storm not onlychanges location according to the storm parameters and/or severityindices, but also changes its characteristics according to thehistorical and/or predicted future storm related parameters and/orimpact indices. For example, if a particular storm if predicted tochange from a tornadic-type storm (e.g., a triangle icon) to a hail-typestorm (e.g., a diamond icon), the display 900 could show a change in theicon type and/or other visual indicators (e.g., intensity) about thestorm at the predicted time and/or location at which such change ispredicted to occur.

In another embodiment, the display aspect of the weather severity andcharacterization system 700 includes the assessing the impact of theidentified storm(s) on people and property based on the historical,current or predicted parameters of the storms, including location,severity and other characteristics. The general concepts of assessingthe impact of weather on people and property is discussed in detail inco-pending U.S. Pat. No. 7,181,346, entitled System and Method forAssessing The People and Property Impact of Weather, filed Mar. 31,2005, the entire disclosure of which is incorporated herein byreference. The concepts discussed therein are applicable to the weatherseverity and characterization system 700 in that a more accurateassessment and/or display of the potential impact to people and propertymay be obtained by incorporating the storm related parameters and impactindices discussed herein.

The storm related display obtained through the weather severity andcharacterization system 700 may include one or a combination of theavailable storm related parameters or impact indices. That is, dependingon the display type and configuration, all of the impact indices may bedisplayed. Alternatively, only some subset of the impact indices may bedisplayed (e.g., those indices specifically related to the type ofidentified storm). In an alternative embodiment, one or more of thestorm related parameters (e.g., hail size, lightning flash rate,convective energy, etc.) may also be displayed.

In one embodiment, the icon or marking denoting the selected storm ishighlighted or otherwise altered (not shown) to indicate that thatparticular storm has been selected and that the present severityparameters correspond to that storm.

In one embodiment, the weather severity and characterization system 700navigates through a sequence of identified storms based on the stormrelated parameters and/or the impact indices. That is the display 900may show, for example, a set number of the most severe storms (asmeasured by the composite severity index 930) within a geographic regionof interest 902. The weather severity and characterization system 700may then automatically or by presenter or viewer initiated input (e.g.,a virtual “button” or link on the display 900) sequence through thedesignated storms in an order defined by the weather severity andcharacterization system 700. For example, the display 900 may includethe five most severe storms in a region and proceed to highlight each ofthe identified storms (and display corresponding storm relatedparameters and impact indices) in an order of decreasing compositeseverity index 930, such that the most severe storm in the region isdisplayed first.

In one embodiment, in conjunction with a sequencing feature of thedisplay of the weather severity and characterization system 700, thedisplay 900 automatically highlights and displays relevant storm relatedparameters and impact indices related to the identified storms, whileautomatically zooming-in on a particular storm for a period of time oruntil another actuation by the presenter or viewer. The display 900 thenautomatically zooms-out, pans and selects the next relevant storm in thecategory and again zooms-in and displays the relevant storm relatedparameters and impact information.

In another embodiment, the display 900 includes multiple buttons, linksor other selection options (not shown), that may be actuated by thepresenter or viewer. For example, the display may include selectionoptions for each of the potential impact indices. Selection of one ofsuch options initiates the display of identified storms relevant to theselected category. For example, the display 900 may include a “HailStorm Impact” button (not shown). Actuation of such button wouldinitiate the display of a pre-set number of the identified storms thathave the greatest past, current and/or predicted future hail impact asmeasured by the hail severity index 928.

Those skilled it the art will recognize that any combination of thepreviously described display options or possibilities may be employed inconjunction with the user-defined, pre-set and/or automatic sequencingfeatures of the weather severity and characterization system 700.

In one embodiment, the display of the impact indices and/or stormrelated parameters is configurable by the presenter. That is, asdescribed above, the presenter may be able to select (or un-select)which impact indices and/or in which format(s) the presenter desires fordisplay. Such configuration selections may be made pre- orpost-selection of the storm of choice. Alternatively, the displayedimpact indices and format may be predetermined through the use ofuser-defined or pre-set rules, similar to the navigation/presentationrules discussed above with respect to the intelligent broadcast system100.

In one embodiment, the presenter or viewer interacts with the displayaspect of the weather severity and characterization system 700 through agraphical user interface (“GUI”) 950, examples of which is shown inFIGS. 30 and 36. The GUI 950 includes a layer menu 952 that enables thepresenter or viewer to add different sets or aspects of data, or layers,to the display 900. For example, referring to FIG. 30, the presenter maydesire to look at radar data, the associated storm icons 906 andassociated storm tracks 908 identified by such radar data, which wouldinvolve adding three layers of information to the display 900. The layercontrols 954 allow the presenter to control particular aspects of aselected layer. An output window 956 shows the present output or display900 as selected in the layer menu 952. A wide view window 958 shows therelevant weather data over an area of interest that is larger than thegeographic region of interest 902 shown in the display 900 or the outputwindow 956. In FIG. 30, for example, the wide view window 958 showsradar data over a large region. The red rectangle 959 depicts the areacurrently displayed on the display 900. A view control window 960 allowsthe user to specify the general domain or region of interest (e.g.,eastern USA or western USA, etc.). An animation control window 962includes one or more buttons allowing the user to run animations of themost recent data, customized to meet the users' needs or preferences(e.g., animation speed, resolution, etc.). An off-air query window 964permits the presenter or user to view or analyze the raw data associatedwith data aspects received as a result of the selections made in thelayer menu 952 without showing such data or analysis on the display 900.

Referring to FIG. 36, the off air query window 964 displays past,present, and future time values for some of the storm related parametersfor the storm represented by the storm icon 906. Also, in FIG. 36, thegraphical text box 912 in the GUI 950 may display storm relatedparameters or other raw data, such as Sfc-based CAPE 980, or Height of50 dbZ 982, instead of the severity indices which would normally bepresented to a viewer in an on-air or Internet presentation. Thus, theweather severity and characterization system 700 allows the end-user(e.g. the presenter, user or operator) to view the complete set of datain a way not intended for, or visible to, public presentation ordisplay, thereby enabling, for example, an expert (e.g., meteorologist)with access to the storm related parameters and impact indices foroff-camera or “behind the scenes” meteorological analysis.

In one embodiment, the display of audio or visual data in thepresentation is permitted. For instance, the presenter 102 could selecta particular storm 702 from the display 900. The general location of thestorm and the time of the weather data observations could be retrievedfrom the storm database 760. The presentation system could then searchthe storm database 760 for corresponding images. These images may havebeen uploaded to the storm identification and characterization system740 by mobile users 784 using their hand-held or portable devices 782and the wireless network 780. Thus, the presenter may be able to displayimages or videos taken at the scene by eyewitnesses as a part of histimely weather presentation.

FIG. 15 is a use case diagram of one embodiment of the weather severityand characterization system 700. The weather severity andcharacterization system 700 includes a retrieve data use case 882through which the presenter 102 may initiate the retrieval of dataand/or identify a particular area or geographic region of interest forwhich to obtain storm related parameters or other weather information.Presenter or user interaction with the retrieve data use case 882includes graphical and/or text input or interaction. For example, thepresenter 1021 may specify a geographic region of interest byhighlighting or selecting such a region on a map or other display in amanner generally known in the art. Alternatively, such a selection maybe made by identifying (through text, graphical, touch-screen, verbal orother methods) a particular radar site or sites, or a particulargeographic area or point and a radius with respect thereto. Thoseskilled in the art will recognize that there are other manners in whichthe presenter 102 could identify a desired geographic region. Theretrieve data use case 882 includes access to and retrieves relevantweather data from a variety or combination of sources described above,including Doppler radar data 1390, satellite data 1392, terrestrialweather data 1394 and historical weather data 130.

An identify storm use case 884 identifies relevant storms within theretrieved data as previously described. A generate storm parameters usecase 886 determines the relevant storm related parameters discussedabove for the storms identified by the identify storm use case 884 usingthe retrieved data. Additionally, the generate storm parameters use case886 may access the convective storm analysis system 1300 and/or thestorm parameters database 760 to generate the relevant storm relatedparameters. The storm related parameters may be stored in the stormparameters database 760.

An assemble storm parameters use case 890 obtains relevant storm relatedparameters from the storm parameters data base 760 (or the generatestorm parameters use case 886) and interacts with the display/monitor104. As described above, the assemble storm parameters use case 890 mayautomatically present the identified storms on the display/monitor 104to the presenter 102 for selection thereof (e.g. in accordance withdefault rules), or may require additional input from the presenter 102or other system (not shown) to determine which of the identified stormsshould be displayed for selection and/or retrieval of additionalinformation, for example, through the set display options use case 894.

The presenter 102 also interacts with the weather severity andcharacterization system 700 via the select storm use case 892 to selectone or more of the storms identified by the identify storms use case 884and for which storm related parameters may have been generated orassembled. Selection of such storms may be accomplished by clicking,highlighting or otherwise selecting an icon 906 described above or viaany other selection mechanism generally known in the art. For example,the presenter 102 need not actually be presented with a list orgraphical identifier of the identified storms, but rather my select astorm by gesturing at a particular area of the desired radar or weatherdata. Upon selection of a storm, the assemble storm parameters use case890 obtains the relevant storm related parameters corresponding to theselected storm(s). The storm related parameters may be displayed to thepresenter 102 on the display/monitor 104 in any of the mannerspreviously described, including according to display options set orselected through the set display options use case 894, to for apresentation of weather information. The weather severity andcharacterization system 700 includes a select storm sequence use case896 through which the presenter 102 may determine which type (e.g.,which storms and criteria) and initiate a sequenced storm presentation,as described above.

The concepts described herein can be applied to a number of types ofdata and presentations including, but not limited to, traffic reportingand integrated weather and traffic reports, business, technical andeconomic presentations, and other displays of data.

The present invention may be implemented with any combination ofhardware and software. If implemented as a computer-implementedapparatus, the present invention is implemented using means forperforming all of the steps and functions described above.

The present invention can be included in an article of manufacture(e.g., one or more computer program products) having, for instance,computer useable media. The media has embodied therein, for instance,computer readable program code means for providing and facilitating themechanisms of the present invention. The article of manufacture can beincluded as part of a computer system or sold separately.

Although the description above contains many specific examples, theseshould not be construed as limiting the scope of the invention but asmerely providing illustrations of some of the presently preferredembodiments of this invention. Thus, the scope of the invention shouldbe determined by the appended claims and their legal equivalents, ratherthan by the examples given.

It will be appreciated by those skilled in the art that changes could bemade to the embodiments described above without departing from the broadinventive concept thereof. It is understood, therefore, that thisinvention is not limited to the particular embodiments disclosed, but itis intended to cover modifications within the spirit and scope of theembodiments of the present invention.

1. A method of collecting and storing information relating to a weather phenomenon using a computing system comprising at least one computer, the method comprising: (a) obtaining, by the at least one computer, a set of weather data comprising weather data from at least one primary source and one or more secondary sources; (b) identifying, by the at least one computer, one or more instances of weather phenomena using the weather data from the at least one primary source; (c) retrieving, by the at least one computer, historical values corresponding to one or more past times for a first set of parameters related to each instance of the identified weather phenomena if the instance of the weather phenomenon has been identified for at least one of the past times; (d) obtaining, by the at least one computer, current values corresponding to the present time for a second set of parameters related to each instance of the weather phenomena identified in step (b) by applying a first set of one or more algorithms to the set of weather data; (e) predicting, by the at least one computer, forecasted values corresponding to at least one future time for the second set of parameters for each instance of the weather phenomena identified in step (b) by applying a second set of one more of algorithms to the set of weather data; (f) determining, by the at least one computer, values for a set of severity indices corresponding to the present and future times for each instance of the weather phenomena based at least in part on the present and future values of the second set of parameters; and (g) assimilating, by the at least one computer, in a multi-dimensional database the retrieved values from step (c), the present time values of the second set of parameters, and the future time values for the second set of parameters, and the values for the severity indices from step (f) for each of the one or more instances of the identified weather phenomena.
 2. The method of claim 1, further comprising: (h) iteratively repeating steps (a) through (g) at future times, wherein the repeating of steps (a) through (g) further comprises appending to the multi-dimensional database an aggregated set of data created in step (g) for each of the one or more instances of the identified weather phenomena.
 3. The method of claim 1, wherein the forecasted values are obtained based at least in part on the historical and current values.
 4. The method of claim 1, wherein the forecasted values correspond to one or more projected states of the one or more instances of the weather phenomenon.
 5. The method of claim 1, wherein the weather phenomena include convective storms.
 6. The method of claim 1, wherein the predicting in step (e) further includes applying the second set of one more of algorithms to the set of weather data, the current value obtained in step (c), and the historical values retrieved in step (b).
 7. The method of claim 1, wherein the at least one primary source provides weather radar data.
 8. The method of claim 7, wherein the at least one secondary source provides non-radar weather data.
 9. The method of claim 8, wherein the future values of the at least one weather phenomenon are determined by synthesizing the data obtained from the at least one primary source and the at least one secondary source.
 10. The method of claim 1, wherein the set of severity indices includes a composite severity index comprising an aggregate representation of at least two of the other severity indices.
 11. The method of claim 10, wherein the severity indices include at least one of lightning severity, tornado severity, flooding severity, hail severity, and wind severity of the instance of the weather phenomenon, each severity index corresponding to a normalized indication of an impact to at least one of property, population, and infrastructure from a different threat associated with the instance of the weather phenomenon.
 12. A method of collecting and storing information for a weather phenomenon using a computing system comprising at least one computer, the method comprising: (a) obtaining, by the at least one computer, a set of weather data; (b) identifying, by the at least one computer, at least one instance of a weather phenomenon using the set of weather data; (c) calculating, by the at least one computer, using the set of weather data, values for a set of parameters that substantially describe the characteristics of each of the at least one instances of a weather phenomenon, wherein the set of parameters includes a set of severity indices for each weather phenomenon instance, the set of severity indices including a composite severity index comprising an aggregate representation of at least two of the other severity indices; and (d) assimilating, by the at least one computer, in a multi-dimensional database the values for the set of the parameters.
 13. The method of claim 12, wherein values of the severity indices are calculated using other parameters in the set of parameters.
 14. The method of claim 12, further comprising: (e) calculating new values for both the set of parameters including the set of severity indices for each instance of the weather phenomenon based on obtaining a second set of weather data; and (f) assimilating the new values into the in the multi-dimensional database.
 15. The method of claim 12, wherein values corresponding to one or more past times are retrieved for previously identified instances of the weather phenomena using weather data obtained prior to the weather data obtained in step (a).
 16. The method of claim 12, wherein the values include values corresponding to the present time for the set of the parameters.
 17. The method of claim 12, wherein the values include predicted future values for the set of parameters.
 18. The method of claim 12, wherein the weather phenomena include thunderstorms.
 19. A method of collecting and storing data about a convective storm using a computing system comprising at least one computer, the method comprising: (a) associating, by the at least one computer, with one or more instances of a convective storm a plurality of parameters for one or more past times, the present time, and one or more future times, wherein the parameters are obtained from a set of weather data, the convective storm instances being identified from the weather data; and (b) assimilating, by the at least one computer, in a multi-dimensional database the plurality of parameters for each instance of the convective storm, wherein the plurality of parameters includes a set of severity indices determined from at least some of the parameters, each severity index describing a normalized impact to at least one of property, population, and infrastructure from a different threat associated with the instance of the convective storm.
 20. The method of claim 19, wherein the severity indices include a composite severity index, the composite severity index being an aggregate of at least two of the other severity indices.
 21. The method of claim 19, wherein the parameters for the present and future times are determined using some of the parameters for the past times.
 22. The method of claim 19, wherein the set of weather data is obtained from one or more primary sources of weather data and one or more secondary sources of weather data.
 23. The method of claim 22, wherein the primary source provides weather radar data and wherein the secondary source provides non-radar weather data.
 24. The method of claim 23, wherein the dimensionality of the multi-dimensional database is reduced by one by combining duplicate identifications of the same instance of the at least one weather phenomenon from primary sources corresponding to different geographic locations.
 25. The method of claim 24, wherein the duplicate identification of the same instance of the at least one weather phenomenon includes separate identifications of the same convective storm obtained from different sets of weather data obtained from two or more different weather radar stations.
 26. An article of manufacture for collecting and storing information for a weather phenomenon, the article of manufacture comprising a computer-readable storage medium holding computer-executable instructions for performing a method comprising: (a) obtaining a set of weather data; (b) identifying at least one instance of a weather phenomenon using the set of weather data; (c) calculating, using the set of weather data, values for a set of parameters that substantially describe the characteristics of each of the at least one instances of a weather phenomenon, wherein the set of parameters includes a set of severity indices for each weather phenomenon instance, the set of severity indices including a composite severity index comprising an aggregate representation of at least two of the other severity indices; and (d) assimilating in a multi-dimensional database the values for the set of the parameters.
 27. The article of manufacture of claim 26, further comprising: (e) calculating new values for both the set of parameters including the set of severity indices for each instance of the weather phenomenon based on obtaining a second set of weather data; and (f) assimilating the new values into the in the multi-dimensional database.
 28. The article of manufacture of claim 26, wherein values corresponding to one or more past times are retrieved for previously identified instances of the weather phenomena using weather data obtained prior to the weather data obtained prior to the weather data obtained in step (a).
 29. A computing system comprising at least one computer for collecting and storing information for a weather phenomenon, the computing system comprising: means for obtaining a set of weather data; means for identifying at least one instance of a weather phenomenon using the set of weather data; means for calculating, using the set of weather data, values for a set of parameters that substantially describe the characteristics of each of the at least one instances of a weather phenomenon, wherein the set of parameters includes a set of severity indices for each weather phenomenon instance, the set of severity indices including a composite severity index comprising an aggregate representation of at least two of the other severity indices; and means for assimilating in a multi-dimensional database the values for the set of the parameters. 